# Operation Reference
This reference is generated from `noema_lab.ops.build_registry()` and each operation's `describe()` contract.
Operation contracts are the implementation-adjacent source of truth for recipe validation, graph rendering, training inspection, and UI parameter panels.
`differentiability.trainable_params` is legacy compatibility metadata. The independent `training_capabilities` values below govern built-in fine-tuning and portable replacement.
## channel
### `channel.bit_boundary`
**Name:** Canonical bit boundary checkpoint
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `expected_bit_count` | `integer` | no | default `0` | |
| `label` | `string` | no | default `boundary` | |
| `role` | `string` | no | default `channel_boundary` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
This fixed-point check validates discrete uint8 bits; it is exact for benchmarking but stops gradients.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `cpp` |
| `dataset_capture` | `numpy`, `cpp` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Canonical bit boundaries must preserve unpacked uint8 bit values exactly.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.bit_count_match`
**Name:** Channel bit-count consistency check
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
| `reference` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `channel_io` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.bits_to_indices`
**Name:** Unpack bits into semantic indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `invalid_policy` | `string` | no | default `mod`
values `mod`, `clamp` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.bits_to_latents`
**Name:** Unpack payload bits into continuous semantic latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `sanitize` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.capacity_oracle_digital_link`
**Name:** Capacity-oracle protected digital AWGN link
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `channel` | `string` | no | default `awgn`
values `awgn` | |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `capacity_oracle_awgn` | |
| `ldpc_rate` | `number` | no | default `0.5` | |
| `modulation` | `string` | no | default `qpsk`
values `qpsk` | |
| `on_decode_failure` | `string` | no | default `gray_image`
values `gray_image`, `erasure`, `report_outage` | |
| `snr_db` | `number` | no | default `12.0` | |
**Differentiability (legacy `trainable_params`):** framework=`none`, gradient=`none`, trainable_params=`False`, exportable=`False`
This is a theoretical protected-digital reference, not a differentiable physical link or implemented LDPC decoder.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.communication_resource_accounting`
**Name:** Communication resource accounting boundary
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy`, `channel.bits.numpy` |
| `framed_bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `payload_bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `physical_header_symbol_count` | `integer` | no | default `0` | |
| `pilot_symbol_count` | `integer` | no | default `0` | |
| `total_channel_use_count` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.communication_resource_accounting.v2`
**Name:** Communication resource accounting boundary (strict kinds v2)
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy` |
| `framed_bits` | `channel.framed_bits.numpy` |
| `payload_bits` | `channel.payload_bits.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `physical_header_symbol_count` | `integer` | no | default `0` | |
| `pilot_symbol_count` | `integer` | no | default `0` | |
| `total_channel_use_count` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.crc32_check`
**Name:** Check CRC32 packets and recover payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy`, `channel.demod_bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `on_decode_failure` | `string` | no | default `gray_image`
values `gray_image`, `erasure`, `report_outage` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
CRC checking and packet erasure are hard non-differentiable decisions.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.csi_feedback_link`
**Name:** Explicit CSI feedback link
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `feedback_code` | `channel.csi_feedback_code.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `received_code` | `channel.csi_feedback_received.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `bits_per_latent` | `integer` | no | default `8` | |
| `clip_value` | `number` | no | default `1.0` | |
| `mode` | `string` | no | default `ideal_noiseless`
values `ideal_noiseless`, `uniform_quantized` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`False`, exportable=`True`
The ideal link is differentiable; fixed-bit quantization requires an externally chosen surrogate such as a straight-through estimator during training.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "float32"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `channel.identity_decoder`
**Name:** Identity channel decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.identity_encoder`
**Name:** Identity channel encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.framed_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.identity_link`
**Name:** Disabled channel identity bit link
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `identity_channel` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
This is a discrete bitstream bypass. Disabled physical channels for differentiable symbol paths should use channel.identity_symbol_link.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Bit-perfect bypass materializations must preserve unpacked uint8 bits exactly.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.identity_symbol_link`
**Name:** Disabled channel identity symbol link
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `label` | `string` | no | default `identity_symbol_channel` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Disabled continuous-symbol channels are identity maps in differentiable export.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Identity PHY materializations must return the same continuous symbols.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `channel.indices_to_bits`
**Name:** Pack semantic indices into bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.jpeg_capacity_oracle`
**Name:** JPEG + ideal-capacity separation
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `channel_model` | `string` | no | default `awgn`
values `awgn`, `slow_rayleigh` | awgn uses the average-SNR capacity directly. slow_rayleigh fixes the source rate from average SNR and tests it against one unknown block-fading gain per image. |
| `channel_uses_per_pixel` | `number` | no | default `0.5` | Fixed complex channel uses available per spatial source pixel. |
| `maximum_quality` | `integer` | no | default `95` | |
| `minimum_quality` | `integer` | no | default `1` | |
| `on_outage` | `string` | no | default `gray_image`
values `gray_image`, `zeros`, `image_channel_mean` | |
| `optimize` | `boolean` | no | default `False` | |
| `progressive` | `boolean` | no | default `False` | |
| `seed` | `integer` | no | | Optional explicit channel seed. Use the same value on a paired learned recipe to reproduce per-image fading gains. |
| `snr_db` | `number` | no | default `12.0` | |
| `subsampling` | `string` | no | default `420`
values `keep`, `444`, `422`, `420` | |
**Differentiability (legacy `trainable_params`):** framework=`none`, gradient=`none`, trainable_params=`False`, exportable=`False`
This is a theoretical separation reference combining classical JPEG with an ideal capacity-achieving channel code.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "JPEG Huffman coding",
"coder": "Huffman",
"implementation": "Pillow JPEG backend",
"language": "C/Python",
"note": "Baseline JPEG comparisons normally use non-progressive JPEG without optimized Huffman-table search."
}
```
### `channel.latents_to_bits`
**Name:** Pack continuous semantic latents into payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `dtype` | `string` | no | default `float32`
values `float16`, `float32` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.nr_ldpc_decoder`
**Name:** 3GPP NR transport-block LDPC soft decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `llr` | `channel.llr.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_tb_crc_failure` | `string` | no | default `zero_fill`
values `zero_fill`, `keep_estimate`, `raise` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`stop`, trainable_params=`False`, exportable=`False`
The publication receiver returns hard bits and CRC decisions.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `sionna` |
| `dataset_capture` | `sionna` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "sionna",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.nr_ldpc_encoder`
**Name:** 3GPP NR transport-block LDPC encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.framed_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `channel_type` | `string` | no | default `PUSCH`
values `PUSCH`, `PDSCH` | |
| `codeword_index` | `integer` | no | default `0`
values `0`, `1` | |
| `n_id` | `integer` | no | default `1` | |
| `n_rnti` | `integer` | no | default `1` | |
| `num_bits_per_symbol` | `integer` | no | default `2`
values `1`, `2`, `4`, `6`, `8` | |
| `num_bp_iter` | `integer` | no | default `20` | |
| `num_layers` | `integer` | no | default `1` | |
| `target_coderate` | `number` | no | default `0.5` | |
| `transport_block_size_bits` | `integer` | no | default `16000` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`stop`, trainable_params=`False`, exportable=`False`
CRC attachment, segmentation, rate matching, and hard bit coding are discrete.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `sionna` |
| `dataset_capture` | `sionna` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Publication use requires independent TS 38.212 vectors or a second standards implementation; internal round trips alone are not conformance.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "sionna",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.packetize_crc32`
**Name:** Packetize payload bits with CRC32
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `packet_payload_bits` | `integer` | no | default `4096` | Payload bits protected by one CRC32. The final packet is padded but its valid length is checked. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
CRC packetization is a hard digital transport operation.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.packetize_crc32.v2`
**Name:** Packetize payload bits with CRC32 (framed-bit contract v2)
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.framed_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `packet_payload_bits` | `integer` | no | default `4096` | Payload bits protected by one CRC32. The final packet is padded but its valid length is checked. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
CRC packetization is a hard digital transport operation.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.payload_passthrough_decoder`
**Name:** Payload bitstream pass-through decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.payload_passthrough_encoder`
**Name:** Payload bitstream pass-through encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.repetition_decoder`
**Name:** Repetition channel decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `factor` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.repetition_encoder`
**Name:** Repetition channel encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.framed_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `coded_bits` | `channel.coded_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `factor` | `integer` | no | default `3` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.symbol_boundary`
**Name:** Canonical complex-symbol boundary checkpoint
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `expected_symbol_count` | `integer` | no | default `0` | |
| `label` | `string` | no | default `symbol_boundary` | |
| `role` | `string` | no | default `channel_symbol_boundary` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Differentiable export treats this fixed-point check as a typed identity for continuous symbols; benchmark execution validates and serializes complex64 symbols.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Symbol boundaries are identity checks; numeric implementations must preserve complex symbol values.",
"tolerance": {
"atol": 0.0,
"rtol": 0.0
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `channel.symbol_count_match`
**Name:** Channel symbol-count consistency check
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
| `reference` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `label` | `string` | no | default `symbol_channel_io` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `channel.symbol_power_identity`
**Name:** Transmit power disabled pass-through
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `label` | `string` | no | default `tx_power_off` | |
| `power_unit` | `string` | no | default `normalized`
values `normalized`, `mW` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Disabled TX power is a continuous-symbol identity block; it preserves the PHY skeleton and does not change transmit power.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "The disabled TX power block must pass canonical complex symbols through unchanged.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_symbol_identity",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_symbol_identity",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_identity",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `channel.symbol_power_normalize`
**Name:** Transmit symbol power normalization
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `eps` | `number` | no | default `1e-12` | |
| `label` | `string` | no | default `tx_power_normalize` | |
| `normalization_scope` | `string` | no | default `source_item`
values `source_item`, `global` | Normalize each declared source item independently, or normalize the complete tensor/stream as one global batch. |
| `target_power` | `number` | no | default `1.0` | Normalized average complex-symbol power target applied by this normalizer; this is not a value in watts unless a physical link-budget conversion is defined. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Continuous-symbol normalization is a differentiable scale operation that fixes average transmit power before the physical channel.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "NumPy and Torch materializations should produce the same average-power-normalized symbols within floating-point tolerance.",
"tolerance": {
"atol": 1e-06,
"rtol": 1e-05
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_source_item_power_normalizer",
"parameter_bindings": {
"normalization_scope": "source_item"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_global_power_normalizer",
"parameter_bindings": {
"normalization_scope": "global"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_source_item_power_normalizer",
"parameter_bindings": {
"normalization_scope": "source_item"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_global_power_normalizer",
"parameter_bindings": {
"normalization_scope": "global"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_source_item_power_normalizer",
"parameter_bindings": {
"normalization_scope": "source_item"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_global_power_normalizer",
"parameter_bindings": {
"normalization_scope": "global"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
## demodulation
### `demodulation.digital_demodulate`
**Name:** Digital symbol-to-bit demodulator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `allocation` | `channel.power_allocation.numpy` |
| `channel_state` | `channel.ofdm_channel_state.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.demod_bits.numpy` |
| `llr` | `channel.llr.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `modulation` | `string` | no | default `auto`
values `auto`, `bpsk`, `qpsk`, `qam16` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Hard demodulation decisions stop gradients; use soft/differentiable demodulation or dataset capture mode for neural receiver training.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `cpp` |
| `dataset_capture` | `numpy`, `cpp` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Demapper materializations should produce matching hard bits and comparable LLR-like reliability values for the same symbols.",
"tolerance": {
"atol": 1e-07,
"rtol": 1e-06
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_auto_demodulator",
"parameter_bindings": {
"modulation": "auto"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_bpsk_demodulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qpsk_demodulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qam16_demodulator",
"parameter_bindings": {
"modulation": "qam16"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_bpsk_demodulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_qpsk_demodulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_auto_demodulator",
"parameter_bindings": {
"modulation": "auto"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_bpsk_demodulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qpsk_demodulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qam16_demodulator",
"parameter_bindings": {
"modulation": "qam16"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_bpsk_demodulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_qpsk_demodulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `demodulation.identity_demodulate`
**Name:** Identity PHY symbol-to-bit mapper
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.demod_bits.numpy` |
| `llr` | `channel.llr.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `identity_phy_demodulator` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`False`, exportable=`True`
Artifact execution thresholds 0/1 identity PHY symbols back to bits. Differentiable export may keep a continuous soft/logit materialization until a loss, but hard bit decisions are surrogate-only.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "When paired with identity modulation and identity channel, demodulation recovers the original uint8 bit vector exactly.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `demodulation.neural_receiver_adapter`
**Name:** Neural receiver adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.demod_bits.numpy` |
| `llr` | `channel.llr.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `neural_receiver` | Entrypoint in the registered trained artifact used for inference. |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained artifact implementing the neural-receiver slot ABI. |
| `artifact_package_sha256` | `string` | no | default `` | Registry-independent digest of the complete trained-artifact package bound into the execution plan. |
| `checkpoint_path` | `string` | no | default `` | |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `mode` | `string` | no | default `reference_qpsk`
values `reference_qpsk`, `oracle_frontend_calibrated`, `linear_npz`, `learned_artifact` | |
| `modulation` | `string` | no | default `auto`
values `auto`, `qpsk` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
Checkpoint-backed neural receivers can be trained/exported; artifact benchmark mode records hard decisions and treats them as gradient-stopping evidence.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "neural_receiver",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact",
"modulation": "qpsk"
},
"component_id": "receiver",
"component_role": "neural_receiver",
"entrypoint_id": "neural_receiver",
"inputs": {
"rx_symbols_ri": {
"dtype": "float32",
"shape": [
"symbol",
2
]
}
},
"outputs": {
"bit_llr": {
"dtype": "float32",
"shape": [
"symbol",
2
]
}
},
"required_operation_inputs": [
"rx_symbols"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "External neural receiver checkpoints are compared by declared BER/BLER behavior rather than exact implementation internals.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "npz|onnx",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "reference_qpsk_receiver",
"notes": "Built-in reference QPSK receiver for smoke benchmarks.",
"parameter_bindings": {
"mode": "reference_qpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "calibrated_iq_oracle_receiver",
"notes": "Diagnostic oracle that inverts the simulated fixed I/Q front-end transform before QPSK demapping.",
"parameter_bindings": {
"mode": "oracle_frontend_calibrated"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_npz_receiver",
"notes": "Tiny linear NPZ checkpoint adapter for smoke benchmarks.",
"parameter_bindings": {
"mode": "linear_npz"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "reference_qpsk_receiver",
"notes": "Produces demodulated bits and LLR-like logits while preserving rx-symbol capture compatibility.",
"parameter_bindings": {
"mode": "reference_qpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "calibrated_iq_oracle_receiver",
"notes": "Uses simulation-only front-end calibration metadata for a diagnostic upper-bound receiver.",
"parameter_bindings": {
"mode": "oracle_frontend_calibrated"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_npz_receiver",
"notes": "Runs the linear NPZ checkpoint adapter while preserving capture compatibility.",
"parameter_bindings": {
"mode": "linear_npz"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"notes": "Runs a schema-v2, hash-pinned neural-receiver artifact through its operation-owned tensor ABI.",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"notes": "The same returned artifact can be used in ordinary capture recipes without a task-specific runner.",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "neural_receiver_training_endpoint",
"notes": "Training-contract export exposes this typed receiver slot; the researcher supplies the trainable module.",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `demodulation.phase_tracking_receiver_adapter`
**Name:** Pilot-aided QPSK phase-tracking receiver
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `pilot_context` | `channel.qpsk_pilot_context.numpy` |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `phase_truth` | `channel.carrier_phase_truth.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.demod_bits.numpy` |
| `diagnostics` | `receiver.phase_tracking_diagnostics.numpy` |
| `llr` | `channel.llr.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `phase_tracking_receiver` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 artifact implementing the packet-context phase-tracking receiver ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `pilot_smoothing`
values `uncompensated`, `pilot_interpolation`, `pilot_smoothing`, `decision_directed_pll`, `oracle`, `learned_artifact` | |
| `pilot_smoothing_nearest_pilots` | `integer` | no | default `5` | Number of nearest public pilots used by the noncausal local-linear packet smoother. |
| `pll_alpha` | `number` | no | default `0.12` | |
| `pll_beta` | `number` | no | default `0.005` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The adapter exposes a packet-context portable receiver slot while classical modes remain reproducible baselines.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "phase_tracking_receiver",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "receiver",
"component_role": "phase_tracking_receiver",
"entrypoint_id": "phase_tracking_receiver",
"inputs": {
"receiver_features_v3": {
"dtype": "float32",
"shape": [
"packet",
"frame_symbol",
11
]
}
},
"outputs": {
"residual_phase_rad": {
"dtype": "float32",
"shape": [
"packet",
"frame_symbol"
]
}
},
"required_operation_inputs": [
"rx_symbols",
"pilot_context"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Phase-tracking receivers are compared by paired BER/BLER behavior, not sample-identical internal estimates.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "onnx",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "uncompensated_phase_tracking_receiver",
"parameter_bindings": {
"mode": "uncompensated"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "pilot_interpolation_phase_tracking_receiver",
"parameter_bindings": {
"mode": "pilot_interpolation"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "pilot_smoothing_phase_tracking_receiver",
"parameter_bindings": {
"mode": "pilot_smoothing"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "decision_directed_pll_phase_tracking_receiver",
"parameter_bindings": {
"mode": "decision_directed_pll"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "oracle_phase_tracking_receiver",
"parameter_bindings": {
"mode": "oracle"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "uncompensated_phase_tracking_receiver",
"parameter_bindings": {
"mode": "uncompensated"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "pilot_interpolation_phase_tracking_receiver",
"parameter_bindings": {
"mode": "pilot_interpolation"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "pilot_smoothing_phase_tracking_receiver",
"parameter_bindings": {
"mode": "pilot_smoothing"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "decision_directed_pll_phase_tracking_receiver",
"parameter_bindings": {
"mode": "decision_directed_pll"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "oracle_phase_tracking_receiver",
"parameter_bindings": {
"mode": "oracle"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_packet_context_receiver",
"notes": "Simulator phase truth is not passed to the learned entrypoint.",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_packet_context_receiver",
"notes": "Simulator phase truth is not passed to the learned entrypoint.",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "phase_tracking_receiver_training_endpoint",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
## foundation
### `foundation.clip_image_embed`
**Name:** CLIP-style image embedding adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `embeddings` | `foundation.embedding.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `backend` | `string` | no | default `local_semantic`
values `local_semantic`, `local_color_histogram`, `transformers_clip` | |
| `bins` | `integer` | no | default `16` | |
| `device` | `string` | no | default `cpu` | |
| `dimensions` | `integer` | no | default `8` | |
| `model_id` | `string` | no | default `openai/clip-vit-base-patch32` | |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.clip_retrieval_rank`
**Name:** CLIP retrieval ranker
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `image_embeddings` | `foundation.embedding.numpy`, `vision.embedding.clip.numpy`, `multimodal.embedding.numpy` |
| `targets` | `retrieval.targets.json` |
| `text_embeddings` | `foundation.embedding.numpy`, `vision.embedding.clip.numpy`, `multimodal.embedding.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `rankings` | `retrieval.rankings.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `normalize` | `boolean` | no | default `True` | |
| `top_k` | `integer` | no | default `10` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.clip_text_embed`
**Name:** CLIP-style text embedding adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `embeddings` | `foundation.embedding.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `backend` | `string` | no | default `local_semantic`
values `local_semantic`, `local_hash`, `transformers_clip` | |
| `device` | `string` | no | default `cpu` | |
| `dimensions` | `integer` | no | default `128` | |
| `model_id` | `string` | no | default `openai/clip-vit-base-patch32` | |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.diffusion_state_to_image`
**Name:** Diffusion SemanticState-to-image adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `backend` | `string` | no | default `diffusers`
values `diffusers` | |
| `device` | `string` | no | default `cpu` | |
| `disable_safety_checker` | `boolean` | no | default `False` | |
| `guidance_scale` | `number` | no | default `7.5` | |
| `height` | `integer` | no | default `512` | |
| `model_id` | `string` | no | default `segmind/tiny-sd` | |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
| `negative_prompt` | `string` | no | default `low quality, blurry, abstract, distorted, text, watermark` | |
| `num_inference_steps` | `integer` | no | default `20` | |
| `prompt_template` | `string` | no | default `A realistic photograph: {prompt}` | |
| `use_safetensors` | `boolean \| null` | no | default `None` | |
| `width` | `integer` | no | default `512` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.knowledge_base`
**Name:** Foundation knowledge-base source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `kb` | `foundation.kb.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `facts_json` | `string` | no | default `` | Optional JSON list of facts. Used when kb_id is inline_json; appended for semantic_text_smoke_kb. |
| `kb_id` | `string` | no | default `semantic_text_smoke_kb`
values `semantic_text_smoke_kb`, `empty`, `inline_json` | |
| `max_builtin_concepts` | `integer` | no | default `12` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.sam_segment`
**Name:** SAM-style image segmentation adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `backend` | `string` | no | default `local_grid`
values `local_grid` | |
| `grid_size` | `integer` | no | default `2` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.semantic_state_ground`
**Name:** Ground SemanticState against a knowledge base
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `kb` | `foundation.kb.json` |
| `state` | `semantic.state.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `max_matches_per_state` | `integer` | no | default `12` | |
| `min_token_overlap` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.semantic_state_payload_decode`
**Name:** SemanticState payload decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_error` | `string` | no | default `replace`
values `replace`, `fail` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.semantic_state_payload_encode`
**Name:** SemanticState payload encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `payload_format` | `string` | no | default `semantic_state_json_utf8`
values `semantic_state_json_utf8` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.semantic_state_to_text`
**Name:** SemanticState to text generator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `kb` | `foundation.kb.json` |
| `state` | `semantic.state.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `cache_dir` | `string` | no | default `` | |
| `device` | `string` | no | default `cpu` | |
| `generator` | `string` | no | default `local_template`
values `local_template`, `kb_reconstruct`, `masked_lm` | |
| `model_id` | `string` | no | default `distilbert/distilbert-base-uncased` | |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
| `prefer_source_text` | `boolean` | no | default `True` | |
| `top_k` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.text_mask_repair`
**Name:** Text receiver mask repair
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `cache_dir` | `string` | no | default `` | |
| `device` | `string` | no | default `cpu` | |
| `generator` | `string` | no | default `masked_lm`
values `local_template`, `masked_lm` | |
| `model_id` | `string` | no | default `distilbert/distilbert-base-uncased` | |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
| `top_k` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.text_semantic_state_encode`
**Name:** Text to SemanticState encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `extractor` | `string` | no | default `local_rules`
values `local_rules` | |
| `include_text` | `boolean` | no | default `False` | |
| `max_concepts` | `integer` | no | default `16` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vlm_image_to_state`
**Name:** VLM-style image SemanticState adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `semantic.state.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `backend` | `string` | no | default `local_image_stats`
values `local_image_stats` | |
| `model_id` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vqa_answer_from_packet`
**Name:** VQA answer from semantic packet
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `packet` | `vqa.semantic_packet.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `answers` | `vqa.answers.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `receiver` | `string` | no | default `selected_label`
values `selected_label` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vqa_payload_decode`
**Name:** VQA semantic packet payload decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `packet` | `vqa.semantic_packet.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_error` | `string` | no | default `fail`
values `fail`, `replace` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vqa_payload_encode`
**Name:** VQA semantic packet payload encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `packet` | `vqa.semantic_packet.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `payload_format` | `string` | no | default `json_utf8`
values `json_utf8` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vqa_semantic_select`
**Name:** VQA semantic region selector
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `questions` | `vqa.questions.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `detections` | `vision.detections.json` |
| `packet` | `vqa.semantic_packet.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `selector` | `string` | no | default `local_rules`
values `local_rules` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.vqa_transformers_answer`
**Name:** Pretrained Transformers VQA answerer
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `questions` | `vqa.questions.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `answers` | `vqa.answers.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | cpu or cuda device string. The operation uses transformers.pipeline device mapping. |
| `model_id` | `string` | no | default `dandelin/vilt-b32-finetuned-vqa` | Hugging Face model id for a visual-question-answering pipeline. Practical defaults include dandelin/vilt-b32-finetuned-vqa and Salesforce/blip-vqa-base. |
| `model_revision` | `string` | no | default `` | Full immutable Hugging Face commit. The built-in default model resolves to its pinned commit; other remote models require this explicitly. |
| `top_k` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.yolo_detect`
**Name:** YOLO object detector
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `detections` | `vision.detections.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `confidence` | `number` | no | default `0.25` | |
| `device` | `string` | no | default `cpu` | |
| `expected_model_sha256` | `string` | no | default `` | Required for a remote model URL; known built-in asset names use release-pinned hashes. |
| `iou` | `number` | no | default `0.7` | |
| `model_id` | `string` | no | default `yolo11n.pt` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `foundation.yolo_segment`
**Name:** YOLO instance segmenter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `segmentation` | `vision.segmentation_mask.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `confidence` | `number` | no | default `0.25` | |
| `device` | `string` | no | default `cpu` | |
| `expected_model_sha256` | `string` | no | default `` | Required for a remote model URL; known built-in asset names use release-pinned hashes. |
| `iou` | `number` | no | default `0.7` | |
| `mask_threshold` | `number` | no | default `0.5` | |
| `model_id` | `string` | no | default `yolo11n-seg.pt` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## hardware
### `hardware.receiver_iq_imbalance`
**Name:** Receiver I/Q front-end impairment
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dc_offset_i` | `number` | no | default `0.18` | |
| `dc_offset_q` | `number` | no | default `-0.12` | |
| `gain_imbalance_db` | `number` | no | default `5.0` | I-to-Q amplitude-gain ratio in dB. |
| `phase_offset_deg` | `number` | no | default `20.0` | |
| `quadrature_error_deg` | `number` | no | default `12.0` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
The fixed affine I/Q transform is differentiable, while its simulated calibration parameters remain frozen scenario settings.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "All materializations implement the same frozen real 2x2 I/Q transform and DC offset.",
"tolerance": {
"atol": 1e-06,
"rtol": 1e-06
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "fixed_affine_receiver_iq_impairment",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "fixed_affine_receiver_iq_impairment",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "fixed_affine_receiver_iq_impairment",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
## metrics
### `metrics.aoa_estimation`
**Name:** AoA-estimation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.aoa_estimate.numpy` |
| `problem` | `ai_phy.aoa_problem.numpy`, `ai_phy.aoa_truth.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.beamforming`
**Name:** Beamforming metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.beamforming_decision.numpy` |
| `problem` | `ai_phy.beamforming_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.bit_error_rate`
**Name:** Bit error rate
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
| `reference` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `ber` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.block_error_rate`
**Name:** Block error rate
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
| `reference` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.demod_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `block_size` | `integer` | no | default `1024` | |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `bler` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.captioning`
**Name:** Image captioning task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `text.batch.json`, `text.caption.json` |
| `reference` | `text.batch.json`, `text.caption.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.channel_estimation`
**Name:** Channel-estimation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.channel_estimate.numpy` |
| `problem` | `ai_phy.channel_estimation_problem.numpy`, `ai_phy.channel_estimation_truth.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.classification`
**Name:** Classification task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `task.predictions.json`, `task.labels.json` |
| `reference` | `task.labels.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.csi_feedback`
**Name:** CSI feedback and downlink metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `precoder` | `channel.miso_ofdm_precoder.numpy` |
| `reconstruction` | `channel.miso_ofdm_csi_reconstruction.numpy` |
| `true_csi` | `channel.miso_ofdm_csi.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
NMSE and reconstructed-CSI MRT achievable rate have direct differentiable tensor forms.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "json",
"tensor": "structured metrics"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `metrics.detection`
**Name:** Object detection task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `vision.detections.json` |
| `reference` | `vision.detections.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `confidence_threshold` | `number` | no | default `0.0` | |
| `iou_threshold` | `number` | no | default `0.5` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.embedding_similarity`
**Name:** Paired embedding similarity metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `foundation.embedding.numpy`, `vision.embedding.clip.numpy`, `multimodal.embedding.numpy` |
| `reference` | `foundation.embedding.numpy`, `vision.embedding.clip.numpy`, `multimodal.embedding.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `label` | `string` | no | default `embedding`
values `embedding`, `generation.text_image_clip`, `generation.image_image_clip` | |
| `normalize` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.external_classification`
**Name:** External classification metric
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `task.predictions.json`, `task.labels.json` |
| `reference` | `task.labels.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `dict`
values `dict`, `params`, `none` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.image_delivery_status`
**Name:** Image delivery and denominator status
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_decode_failure` | `string` | no | default `report_outage`
values `report_outage` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.image_reconstruction`
**Name:** Image reconstruction metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `reconstruction` | `image.batch.numpy` |
| `reference` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_range` | `number` | no | default `0.0` | Maximum minus minimum pixel value used by PSNR; 0 infers the canonical range from a shared uint8 ([0,255]) or float32 ([0,1]) dtype. |
| `preview_count` | `integer` | no | default `0` | Opt in to embedding up to four compact reference/reconstruction PNG thumbnails in the metrics report for portable result documentation. |
| `preview_size` | `integer` | no | default `128` | Maximum thumbnail width or height when preview_count is nonzero. |
| `psnr_cap_db` | `number` | no | default `99.0` | Requested finite reporting floor for exact reconstructions. The evaluator raises it when necessary so an exact reconstruction always scores above every finite PSNR representable for the evaluated image domain and shape. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.isac_ofdm`
**Name:** ISAC communication/sensing allocation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `decision` | `isac.ofdm_power_allocation.numpy` |
| `problem` | `isac.ofdm_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.leo_ntn_tracking`
**Name:** LEO-NTN Doppler and handover metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ntn.future_state_decision.numpy` |
| `truth` | `ntn.future_state_truth.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.localization`
**Name:** Localization metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.localization_estimate.numpy` |
| `problem` | `ai_phy.localization_problem.numpy`, `ai_phy.localization_truth.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.modulation_classification`
**Name:** Modulation-recognition metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `prediction` | `ai_phy.modulation_predictions.numpy` |
| `truth` | `ai_phy.modulation_labels.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `confusion_matrix` | `metrics.confusion_matrix.numpy` |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `snr_bin_width_db` | `number` | no | default `2.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Discrete evaluation metrics are terminal evidence.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "json|npz",
"tensor": "numpy.ndarray"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.near_field_focusing`
**Name:** Near-field range-angle and focusing metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `near_field.range_angle_estimate.numpy` |
| `problem` | `near_field.array_observation.numpy` |
| `truth` | `near_field.range_angle_truth.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.nr_ldpc_ofdm_delivery`
**Name:** NR LDPC OFDM delivery metrics (v1)
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `actual_state` | `channel.ofdm_channel_state.numpy` |
| `allocation` | `channel.power_allocation.numpy` |
| `decoded_payload` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `decoder_report` | `metrics.report` |
| `reference_payload` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `tx_symbols` | `channel.symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
CRC decisions and bit-exact payload delivery are discrete evidence boundaries.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "The v1 score is a deterministic count of CRC-gated payload bits and occupied QPSK data resource elements.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "json",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.ofdm_finite_blocklength_allocation`
**Name:** Finite-blocklength delayed-CSI allocation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `actual_state` | `channel.ofdm_channel_state.numpy` |
| `allocation` | `channel.power_allocation.numpy` |
| `transmitter_csi` | `channel.ofdm_channel_state.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `blocklength_channel_uses` | `integer` | no | default `128` | Short-packet blocklength used by the parallel-channel normal approximation. |
| `include_third_order_term` | `boolean` | no | default `True` | Include log2(n)/(2n) in the normal approximation. |
| `target_rate_bps_hz` | `number` | no | default `2.0` | Fixed payload rate per complex channel use. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.ofdm_power_allocation`
**Name:** OFDM power-allocation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `allocation` | `channel.power_allocation.numpy` |
| `state` | `channel.ofdm_channel_state.numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `reference` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `outage_target_spectral_efficiency_bps_hz` | `number` | no | default `2.0` | Target instantaneous Shannon spectral efficiency used for the fading-outage statistic. |
| `transport_block_size_bits` | `integer` | no | default `1024` | Payload bits per independently scored transport block for empirical BLER and goodput. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.resource_allocation`
**Name:** Resource allocation metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.resource_allocation_decision.numpy` |
| `problem` | `ai_phy.resource_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.retrieval`
**Name:** Image-text retrieval task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rankings` | `retrieval.rankings.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `k_values` | `string` | no | default `1,5,10` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.segmentation`
**Name:** Segmentation task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `vision.segmentation_mask.numpy` |
| `reference` | `vision.segmentation_mask.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.semantic_state_faithfulness`
**Name:** SemanticState and KB faithfulness metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `semantic.state.json` |
| `kb` | `foundation.kb.json` |
| `reference` | `semantic.state.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.text_semantic_similarity`
**Name:** Text lexical similarity metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `text.batch.json` |
| `reference` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `metrics.vqa`
**Name:** Visual question answering task metrics
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `vqa.answers.json`, `task.predictions.json`, `task.labels.json` |
| `reference` | `vqa.answers.json`, `task.labels.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `report` | `metrics.report` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## model
### `model.aoa_estimator_adapter`
**Name:** AoA estimator adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.aoa_problem.numpy`, `ai_phy.aoa_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.aoa_estimate.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `aoa_estimator` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained artifact implementing the ULA AoA ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `grid_step_deg` | `number` | no | default `0.25` | |
| `mode` | `string` | no | default `bartlett_reference`
values `music`, `bartlett_reference`, `learned_artifact` | Select MUSIC, Bartlett, or a returned portable AoA estimator. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The adapter accepts a portable learned ULA estimator while MUSIC and Bartlett remain fixed comparison methods.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "aoa_estimator",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "aoa_estimator",
"component_role": "single_source_ula_aoa_estimator",
"entrypoint_id": "aoa_estimator",
"inputs": {
"snapshots_ri": {
"dtype": "float32",
"shape": [
"batch",
"antenna",
"snapshot",
2
]
}
},
"outputs": {
"angles_deg": {
"dtype": "float32",
"shape": [
"batch"
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "music_spatial_spectrum_reference",
"parameter_bindings": {
"mode": "music"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "bartlett_spatial_spectrum_reference",
"parameter_bindings": {
"mode": "bartlett_reference"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "music_spatial_spectrum_reference",
"parameter_bindings": {
"mode": "music"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "bartlett_spatial_spectrum_reference",
"parameter_bindings": {
"mode": "bartlett_reference"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_aoa_estimator_endpoint",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_rt_aoa_training_endpoint",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `model.beamforming_adapter`
**Name:** Beamforming/precoding adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.beamforming_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.beamforming_decision.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `beam_policy` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained artifact implementing the beam-policy ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `codebook_sweep_reference`
values `mrt`, `codebook_sweep_reference`, `learned_artifact` | Select a fixed reference or a returned portable beam-policy artifact. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The adapter accepts a portable learned beam policy while MRT and exhaustive DFT-codebook selection remain fixed comparison methods.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "beam_policy",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "beam_policy",
"component_role": "single_user_miso_beam_policy",
"entrypoint_id": "beam_policy",
"inputs": {
"channels_ri": {
"dtype": "float32",
"shape": [
"batch",
"tx_antenna",
2
]
}
},
"outputs": {
"weights_ri": {
"dtype": "float32",
"shape": [
"batch",
"tx_antenna",
2
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "maximum_ratio_transmission",
"parameter_bindings": {
"mode": "mrt"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "codebook_sweep_reference",
"parameter_bindings": {
"mode": "codebook_sweep_reference"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "maximum_ratio_transmission",
"parameter_bindings": {
"mode": "mrt"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "codebook_sweep_reference",
"parameter_bindings": {
"mode": "codebook_sweep_reference"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_beam_policy_endpoint",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_precoder_training_endpoint",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `model.causal_csi_power_allocator`
**Name:** Causal-history symbol power allocator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
| `channel_state` | `channel.ofdm_channel_state.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `allocation` | `channel.power_allocation.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `allocation_contrast` | `number` | no | default `0.6` | |
| `allocation_lower_power_ratio` | `number` | no | default `0.1` | Minimum per-subcarrier power relative to the mean budget for bounded causal-AR water filling. |
| `allocation_upper_power_ratio` | `number` | no | default `1.9` | Maximum per-subcarrier power relative to the mean budget for bounded causal-AR water filling. |
| `artifact_entrypoint` | `string` | no | default `power_policy` | Entrypoint in the registered artifact manifest used for allocator inference. |
| `artifact_manifest_path` | `string` | no | default `` | Registered trained-artifact manifest implementing the architecture-neutral allocator slot ABI. |
| `artifact_package_sha256` | `string` | no | default `` | Registry-independent digest of the complete trained-artifact package bound into the execution plan. |
| `bit_loading_bpsk_min_snr_db` | `number` | no | default `6.0` | |
| `bit_loading_max_bits_per_symbol` | `integer` | no | default `6`
values `1`, `2`, `4`, `6` | |
| `bit_loading_qam16_min_snr_db` | `number` | no | default `17.0` | |
| `bit_loading_qam64_min_snr_db` | `number` | no | default `23.0` | |
| `bit_loading_qpsk_min_snr_db` | `number` | no | default `10.0` | |
| `budget_mode` | `string` | no | default `fixed_average`
values `fixed_average`, `variable_average` | |
| `checkpoint_format` | `string` | no | default `noema_csi_power_deepset_npz_v1`
values `noema_csi_power_deepset_npz_v1` | |
| `checkpoint_max_bytes` | `integer` | no | default `67108864` | |
| `checkpoint_path` | `string` | no | default `` | Frozen safe-NPZ CSI allocator checkpoint used by policy=learned_checkpoint. |
| `checkpoint_sha256` | `string` | no | default `` | Required lowercase SHA-256 of the frozen allocator checkpoint. |
| `checkpoint_strict` | `boolean` | no | default `True` | |
| `csi_gain_shrinkage` | `number` | no | default `0.6` | For uncertainty-shrunk water filling, blend observed gains toward their per-state mean before allocating power. |
| `csi_prediction_gain_confidence` | `number` | no | default `0.4` | Blend complex-AR predicted gains toward their per-state frequency mean before water filling. |
| `csi_prediction_horizon_ofdm_symbols` | `integer` | no | default `0` | For complex-AR prediction, forecast this many OFDM symbols past the newest causal CSI snapshot. Zero uses the CSI artifact's declared feedback delay. |
| `eps` | `number` | no | default `1e-12` | |
| `granularity` | `string` | no | default `global`
values `global`, `per_symbol`, `per_subcarrier`, `per_stream` | |
| `label` | `string` | no | default `tx_power_allocate` | |
| `max_power` | `number` | no | default `2.0` | |
| `midpoint_snr_db` | `number` | no | default `12.0` | |
| `min_power` | `number` | no | default `0.25` | |
| `model_batch_size` | `integer` | no | default `1024` | Maximum number of independent channel-state examples evaluated in one learned-model inference call. Chunking preserves the complete experiment batch and output order. |
| `policy` | `string` | no | default `snr_sigmoid`
values `fixed`, `snr_sigmoid`, `water_filling`, `observed_csi_water_filling`, `robust_csi_water_filling`, `causal_ar_water_filling`, `causal_ar_box_water_filling`, `learned_checkpoint`, `learned_artifact` | Power-allocation method. Model selection is shown only for learned policies. |
| `power_unit` | `string` | no | default `normalized`
values `normalized`, `mW` | |
| `slope_db` | `number` | no | default `4.0` | |
| `snr_db` | `number` | no | default `12.0` | Fallback reference SNR used only by the SNR-adaptive sigmoid policy when no explicit channel state is connected. |
| `stream_count` | `integer` | no | default `1` | |
| `subcarrier_count` | `integer` | no | default `64` | |
| `target_power` | `number` | no | default `1.0` | Normalized average complex-symbol power budget per resource element; the per-state sum constraint is this value times the subcarrier count. |
| `transport_mode` | `string` | no | default `fixed_modulation`
values `fixed_modulation`, `allocation_aware_bit_loading` | Optionally convert the coded-bit stream into a CSI/allocation-aware OFDM modulation map. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
Continuous-symbol power allocation is differentiable. Noema can benchmark a frozen, CSI-conditioned artifact behind an architecture-neutral runtime ABI while model training remains outside the benchmark executor.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "power_policy",
"artifact_manifest_path": "trained_artifact.yaml",
"budget_mode": "fixed_average",
"granularity": "per_subcarrier",
"policy": "learned_artifact"
},
"component_id": "policy",
"component_role": "power_policy",
"constraint_adapter": "noema_exact_simplex_projection_v1",
"entrypoint_id": "power_policy",
"inputs": {
"average_power_budget": {
"dtype": "float32",
"shape": [
"batch",
1
]
},
"csi_history": {
"dtype": "float32",
"shape": [
"batch",
"history",
"subcarrier",
2
]
},
"noise_variance": {
"dtype": "float32",
"shape": [
"batch",
1
]
}
},
"outputs": {
"allocation_scores": {
"dtype": "float32",
"shape": [
"batch",
"subcarrier"
]
}
},
"required_operation_inputs": [
"channel_state"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Reference NumPy and Torch policies should select the same group power targets and scaled symbols for fixed parameters.",
"tolerance": {
"atol": 1e-06,
"rtol": 1e-05
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "noema_csi_power_deepset_npz_v1",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "fixed_power_allocator",
"parameter_bindings": {
"policy": "fixed"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "snr_sigmoid_power_allocator",
"parameter_bindings": {
"policy": "snr_sigmoid"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "closed_form_water_filling_allocator",
"parameter_bindings": {
"policy": "water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "water_filling_on_observed_csi",
"parameter_bindings": {
"policy": "observed_csi_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "correlation_shrunk_water_filling",
"parameter_bindings": {
"policy": "robust_csi_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_water_filling",
"parameter_bindings": {
"policy": "causal_ar_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_box_constrained_water_filling",
"parameter_bindings": {
"policy": "causal_ar_box_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_policy_plus_noema_projection",
"parameter_bindings": {
"policy": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "fixed_power_allocator",
"parameter_bindings": {
"policy": "fixed"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "snr_sigmoid_power_allocator",
"parameter_bindings": {
"policy": "snr_sigmoid"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "closed_form_water_filling_allocator",
"parameter_bindings": {
"policy": "water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "water_filling_on_observed_csi",
"parameter_bindings": {
"policy": "observed_csi_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "correlation_shrunk_water_filling",
"parameter_bindings": {
"policy": "robust_csi_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_water_filling",
"parameter_bindings": {
"policy": "causal_ar_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_box_constrained_water_filling",
"parameter_bindings": {
"policy": "causal_ar_box_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_policy_plus_noema_projection",
"parameter_bindings": {
"policy": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_budgeted_symbol_allocator",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "safe_npz_csi_deepset_checkpoint",
"parameter_bindings": {
"policy": "learned_checkpoint"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "safe_npz_csi_deepset_checkpoint",
"parameter_bindings": {
"policy": "learned_checkpoint"
},
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.channel_estimator_adapter`
**Name:** Channel estimator adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.channel_estimation_problem.numpy`, `ai_phy.channel_estimation_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.channel_estimate.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `channel_estimator` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained artifact implementing the MIMO-OFDM channel-estimator ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `lmmse_assumed_tap_count` | `integer` | no | default `4` | Fixed exponential-PDP prior used by the practical LMMSE baseline. It is not adapted from hidden channel truth. |
| `mode` | `string` | no | default `linear_mmse_reference`
values `least_squares`, `linear_mmse_reference`, `learned_artifact` | Runnable estimator. The learned mode uses a returned portable artifact; the other modes are fixed baselines. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The estimator slot accepts a portable learned artifact while classical LS and exponential-PDP LMMSE modes remain reproducible baselines.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "channel_estimator",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "estimator",
"component_role": "mimo_ofdm_channel_estimator",
"entrypoint_id": "channel_estimator",
"inputs": {
"ls_estimate_ri": {
"dtype": "float32",
"shape": [
"batch",
"rx_antenna",
"tx_antenna",
"subcarrier",
2
]
},
"noise_variance": {
"dtype": "float32",
"shape": [
"batch",
1
]
},
"pilot_ls_ri": {
"dtype": "float32",
"shape": [
"batch",
"rx_antenna",
"tx_antenna",
"subcarrier",
2
]
},
"pilot_mask": {
"dtype": "float32",
"shape": [
"batch",
"tx_antenna",
"subcarrier"
]
}
},
"outputs": {
"h_hat_ri": {
"dtype": "float32",
"shape": [
"batch",
"rx_antenna",
"tx_antenna",
"subcarrier",
2
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "Adapter implementations are compared by NMSE/task metrics under the declared benchmark protocol.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "least_squares",
"parameter_bindings": {
"mode": "least_squares"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_mmse_reference",
"parameter_bindings": {
"mode": "linear_mmse_reference"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "least_squares",
"parameter_bindings": {
"mode": "least_squares"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_mmse_reference",
"parameter_bindings": {
"mode": "linear_mmse_reference"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_channel_estimator_endpoint",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_receiver_training_endpoint",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.compressai_analysis_encode`
**Name:** CompressAI PyTorch analysis transform to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `metric` | `string` | no | default `mse`
values `mse`, `ms-ssim` | |
| `model` | `string` | no | default `bmshj2018_hyperprior`
values `bmshj2018_factorized`, `bmshj2018_factorized_relu`, `bmshj2018_hyperprior`, `bmshj2018_hyperprior_vbr`, `mbt2018_mean`, `mbt2018_mean_vbr`, `mbt2018`, `mbt2018_vbr`, `cheng2020_anchor`, `cheng2020_attn` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `pretrained` | `boolean` | no | default `True` | |
| `quality` | `integer` | no | default `3` | |
| `quantize_latents` | `boolean` | no | default `False` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
The analysis transform is a PyTorch module; Noema benchmark execution runs pretrained eval parameters.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_aoti_decode`
**Name:** CompressAI AOT Inductor synthesis transform to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_aoti_encode`
**Name:** CompressAI AOT Inductor analysis transform to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `quantize_latents` | `boolean` | no | default `False` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_aoti_entropy_decode`
**Name:** CompressAI AOT Inductor payload decode bits to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "AOT Inductor hyperprior transforms + CompressAI entropy backend",
"language": "AOT Inductor/PyTorch + C++ extension + Python",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_aoti_entropy_encode`
**Name:** CompressAI AOT Inductor payload encode latents to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "AOT Inductor hyperprior transforms + CompressAI entropy backend",
"language": "AOT Inductor/PyTorch + C++ extension + Python",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_decode`
**Name:** CompressAI model-zoo payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros`, `gray_image`, `erasure`, `report_outage` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "CompressAI entropy backend",
"language": "C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_encode`
**Name:** CompressAI model-zoo image encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_model_state_sha256` | `string` | no | default `` | Optional publication pin for the fully materialized CompressAI state after model.update(). |
| `metric` | `string` | no | default `mse`
values `mse`, `ms-ssim` | |
| `model` | `string` | no | default `bmshj2018_hyperprior`
values `bmshj2018_factorized`, `bmshj2018_factorized_relu`, `bmshj2018_hyperprior`, `bmshj2018_hyperprior_vbr`, `mbt2018_mean`, `mbt2018_mean_vbr`, `mbt2018`, `mbt2018_vbr`, `cheng2020_anchor`, `cheng2020_attn` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `pretrained` | `boolean` | no | default `True` | |
| `quality` | `integer` | no | default `3` | |
| `vbr_scale_index` | `integer` | no | default `1` | Only used by CompressAI *_vbr models. Selects the variable-rate scale index s, where larger values usually mean higher rate and quality. |
| `vbr_stage` | `integer` | no | default `2` | Only used by CompressAI *_vbr models. Stage 2 uses the variable-rate path; stage 1 behaves like the base model path. |
| `wire_format` | `string` | no | default `safe_json_base64`
values `safe_json_base64`, `compact_binary_v1` | Artifact-safe JSON/base64 remains the compatibility default; compact_binary_v1 is the bounded communication wire format for transmitted-resource studies. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "CompressAI entropy backend",
"language": "C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_entropy_decode`
**Name:** CompressAI payload decode bits to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Entropy decoding and bitstream parsing are discrete payload-coding steps.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "CompressAI entropy backend",
"language": "C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_entropy_encode`
**Name:** CompressAI payload encode latents to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `metric` | `string` | no | default `mse`
values `mse`, `ms-ssim` | |
| `model` | `string` | no | default `bmshj2018_hyperprior`
values `bmshj2018_factorized`, `bmshj2018_factorized_relu`, `bmshj2018_hyperprior`, `bmshj2018_hyperprior_vbr`, `mbt2018_mean`, `mbt2018_mean_vbr`, `mbt2018`, `mbt2018_vbr`, `cheng2020_anchor`, `cheng2020_attn` | |
| `pretrained` | `boolean` | no | default `True` | |
| `quality` | `integer` | no | default `3` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Entropy coding and bitstream serialization are discrete payload-coding steps.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "CompressAI entropy backend",
"language": "C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_export_aoti`
**Name:** Compile CompressAI PyTorch transforms with AOT Inductor
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `model` | `model.aot_inductor.bundle` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `dynamic_shapes` | `boolean` | no | default `False` | Advanced. Keep off for reproducible selected-shape AOTI packages. |
| `export_height` | `integer` | no | default `0` | Advanced. 0 compiles for the selected data shape after padding. |
| `export_width` | `integer` | no | default `0` | Advanced. 0 compiles for the selected data shape after padding. |
| `metric` | `string` | no | default `mse`
values `mse`, `ms-ssim` | |
| `model` | `string` | no | default `bmshj2018_hyperprior`
values `bmshj2018_factorized`, `bmshj2018_factorized_relu`, `bmshj2018_hyperprior`, `bmshj2018_hyperprior_vbr`, `mbt2018_mean`, `mbt2018_mean_vbr`, `mbt2018`, `mbt2018_vbr`, `cheng2020_anchor`, `cheng2020_attn` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `pretrained` | `boolean` | no | default `True` | |
| `quality` | `integer` | no | default `3` | |
| `validate_export` | `boolean` | no | default `True` | |
| `vbr_scale_index` | `integer` | no | default `1` | |
| `vbr_stage` | `integer` | no | default `2` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_export_onnx`
**Name:** Convert CompressAI PyTorch transforms to ONNX
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `model` | `model.onnx.bundle` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `export_height` | `integer` | no | default `0` | Advanced. 0 exports for the selected data shape after padding. |
| `export_width` | `integer` | no | default `0` | Advanced. 0 exports for the selected data shape after padding. |
| `metric` | `string` | no | default `mse`
values `mse`, `ms-ssim` | |
| `model` | `string` | no | default `bmshj2018_hyperprior`
values `bmshj2018_factorized`, `bmshj2018_factorized_relu`, `bmshj2018_hyperprior`, `bmshj2018_hyperprior_vbr`, `mbt2018_mean`, `mbt2018_mean_vbr`, `mbt2018`, `mbt2018_vbr`, `cheng2020_anchor`, `cheng2020_attn` | |
| `opset` | `integer` | no | default `17` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `pretrained` | `boolean` | no | default `True` | |
| `quality` | `integer` | no | default `3` | |
| `validate_export` | `boolean` | no | default `True` | |
| `vbr_scale_index` | `integer` | no | default `1` | |
| `vbr_stage` | `integer` | no | default `2` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_onnx_cpp_decode`
**Name:** CompressAI ONNX Runtime C++ API latent decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `intra_op_num_threads` | `integer` | no | default `0` | |
| `library_path` | `string` | no | default `` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_onnx_cpp_encode`
**Name:** CompressAI ONNX Runtime C++ API analysis transform to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `intra_op_num_threads` | `integer` | no | default `0` | |
| `library_path` | `string` | no | default `` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
| `quantize_latents` | `boolean` | no | default `False` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_onnx_cpp_entropy_decode`
**Name:** CompressAI ONNX Runtime C++ API payload decode bits to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `intra_op_num_threads` | `integer` | no | default `0` | |
| `library_path` | `string` | no | default `` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "ONNX Runtime hyperprior transforms + CompressAI entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_onnx_cpp_entropy_encode`
**Name:** CompressAI ONNX Runtime C++ API payload encode latents to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `intra_op_num_threads` | `integer` | no | default `0` | |
| `library_path` | `string` | no | default `` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "ONNX Runtime hyperprior transforms + CompressAI entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_onnx_decode`
**Name:** CompressAI ONNX Runtime quantized latent decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_onnx_encode`
**Name:** CompressAI ONNX Runtime analysis transform to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
| `quantize_latents` | `boolean` | no | default `False` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_onnx_entropy_decode`
**Name:** CompressAI ONNX payload decode bits to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "ONNX Runtime hyperprior transforms + CompressAI entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_onnx_entropy_encode`
**Name:** CompressAI ONNX payload encode latents to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "ONNX Runtime hyperprior transforms + CompressAI entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_openvino_decode`
**Name:** CompressAI OpenVINO synthesis transform to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `CPU` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_openvino_encode`
**Name:** CompressAI OpenVINO analysis transform to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `CPU` | |
| `pad_to_multiple` | `integer` | no | default `64` | |
| `quantize_latents` | `boolean` | no | default `False` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.compressai_openvino_entropy_decode`
**Name:** CompressAI OpenVINO payload decode bits to latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `CPU` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `torch_device` | `string` | no | default `cpu` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "OpenVINO hyperprior transforms + CompressAI entropy backend",
"language": "OpenVINO + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_openvino_entropy_encode`
**Name:** CompressAI OpenVINO payload encode latents to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `CPU` | |
| `torch_device` | `string` | no | default `cpu` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range coding / ANS backend",
"coder": "CompressAI entropy bottleneck + GaussianConditional",
"implementation": "OpenVINO hyperprior transforms + CompressAI entropy backend",
"language": "OpenVINO + C++ extension + Python/PyTorch",
"note": "The neural transforms may run in PyTorch, AOT Inductor, ONNX Runtime, or OpenVINO; entropy coding remains the CompressAI bitstream backend."
}
```
### `model.compressai_synthesis_decode`
**Name:** CompressAI PyTorch synthesis transform to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
The synthesis transform is a PyTorch module; Noema benchmark execution runs pretrained eval parameters.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.csi_feedback_decoder`
**Name:** CSI feedback decoder / returned-artifact slot
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `received_code` | `channel.csi_feedback_received.numpy`, `channel.csi_feedback_code.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `reconstruction` | `channel.miso_ofdm_csi_reconstruction.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `decoder` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained-artifact manifest for this paired CSI codec. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `feedback_dimension` | `integer` | no | default `64` | Number of real-valued feedback latents per CSI realization. |
| `runtime` | `string` | no | default `training_interface`
values `training_interface`, `truncated_angular_delay`, `identity`, `learned_artifact` | Use the architecture-neutral training slot, a matched-budget classical angular-delay truncation, the full-CSI identity upper bound, or a returned artifact. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
The recipe declares the feedback-to-CSI interface; external training supplies its architecture.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "decoder",
"artifact_manifest_path": "trained_artifact.yaml",
"runtime": "learned_artifact"
},
"component_id": "decoder",
"component_role": "csi_feedback_decoder",
"entrypoint_id": "decoder",
"inputs": {
"feedback_code": {
"dtype": "float32",
"shape": [
"batch",
"feedback_dimension"
]
}
},
"outputs": {
"csi_hat_ri": {
"dtype": "float32",
"shape": [
"batch",
2,
"tx_antenna",
"subcarrier"
]
}
},
"required_operation_inputs": [
"received_code"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "onnx",
"tensor": "float32"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "identity_full_csi_decoder",
"parameter_bindings": {
"runtime": "identity"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "truncated_angular_delay_decoder",
"parameter_bindings": {
"runtime": "truncated_angular_delay"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_decoder",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "identity_full_csi_decoder",
"parameter_bindings": {
"runtime": "identity"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "truncated_angular_delay_decoder",
"parameter_bindings": {
"runtime": "truncated_angular_delay"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_decoder",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_csi_feedback_decoder_slot",
"parameter_bindings": {
"runtime": "training_interface"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.csi_feedback_encoder`
**Name:** CSI feedback encoder / returned-artifact slot
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `csi` | `channel.miso_ofdm_csi.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `feedback_code` | `channel.csi_feedback_code.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `encoder` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained-artifact manifest for this paired CSI codec. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `feedback_dimension` | `integer` | no | default `64` | Number of real-valued feedback latents per CSI realization. |
| `runtime` | `string` | no | default `training_interface`
values `training_interface`, `truncated_angular_delay`, `identity`, `learned_artifact` | Use the architecture-neutral training slot, a matched-budget classical angular-delay truncation, the full-CSI identity upper bound, or a returned artifact. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
The recipe declares the CSI-to-feedback interface; external training supplies its architecture.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "encoder",
"artifact_manifest_path": "trained_artifact.yaml",
"runtime": "learned_artifact"
},
"component_id": "encoder",
"component_role": "csi_feedback_encoder",
"entrypoint_id": "encoder",
"inputs": {
"csi_ri": {
"dtype": "float32",
"shape": [
"batch",
2,
"tx_antenna",
"subcarrier"
]
}
},
"outputs": {
"feedback_code": {
"dtype": "float32",
"shape": [
"batch",
"feedback_dimension"
]
}
},
"required_operation_inputs": [
"csi"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "onnx",
"tensor": "float32"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "identity_full_csi_encoder",
"parameter_bindings": {
"runtime": "identity"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "truncated_angular_delay_encoder",
"parameter_bindings": {
"runtime": "truncated_angular_delay"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_encoder",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "identity_full_csi_encoder",
"parameter_bindings": {
"runtime": "identity"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "truncated_angular_delay_encoder",
"parameter_bindings": {
"runtime": "truncated_angular_delay"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_encoder",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_csi_feedback_encoder_slot",
"parameter_bindings": {
"runtime": "training_interface"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.csi_mrt_precoder`
**Name:** MRT precoder from reconstructed CSI
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `reconstruction` | `channel.miso_ofdm_csi_reconstruction.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `precoder` | `channel.miso_ofdm_precoder.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Per-subcarrier normalized MRT is differentiable with respect to reconstructed CSI.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "float32 RI"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.deepjscc_external_decode`
**Name:** DeepJSCC decoder replacement / returned-artifact slot
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.rx_symbols.complex_numpy`, `channel.symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `` | Entrypoint in the registered artifact manifest; defaults to encoder or decoder by slot role. |
| `artifact_manifest_path` | `string` | no | default `` | Registered trained-artifact manifest implementing this DeepJSCC slot. |
| `artifact_package_sha256` | `string` | no | default `` | Required package identity of the frozen trained artifact. |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `checkpoint_format` | `string` | no | default `noema_deepjscc_reference_cnn_npz_v1`
values `noema_deepjscc_reference_cnn_npz_v1` | |
| `checkpoint_max_bytes` | `integer` | no | default `67108864` | |
| `checkpoint_path` | `string` | no | default `` | Managed safe-NPZ DeepJSCC checkpoint used by runtime=learned_checkpoint. |
| `checkpoint_sha256` | `string` | no | default `` | Required lowercase SHA-256 of the frozen DeepJSCC checkpoint. |
| `checkpoint_strict` | `boolean` | no | default `True` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
| `runtime` | `string` | no | default `training_interface`
values `training_interface`, `external_callable`, `learned_checkpoint`, `learned_artifact` | Declare an export-only interface, invoke a trusted callable, use the reference checkpoint runtime, or run a registered portable trained artifact. |
| `symbol_channels` | `integer` | no | default `16` | Complex channel count declared by the managed checkpoint. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
This operation defines a portable decoder replacement boundary. When selected, its current implementation is omitted and the researcher-supplied module owns parameters and autograd behavior.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "decoder",
"artifact_manifest_path": "trained_artifact.yaml",
"runtime": "learned_artifact"
},
"component_id": "decoder",
"component_role": "decoder",
"entrypoint_id": "decoder",
"inputs": {
"symbols_ri": {
"dtype": "float32",
"shape": [
"batch",
"real_imag_channel",
"symbol_height",
"symbol_width"
]
}
},
"outputs": {
"reconstruction": {
"dtype": "float32",
"shape": [
"batch",
3,
"height",
"width"
]
}
},
"required_operation_inputs": [
"symbols"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `external`, `torch`, `onnxruntime` |
| `dataset_capture` | `external`, `torch`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "external",
"implementation": "external_callable_runtime",
"parameter_bindings": {
"runtime": "external_callable"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "safe_npz_reference_cnn_runtime",
"parameter_bindings": {
"runtime": "learned_checkpoint"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "external",
"implementation": "external_callable_runtime",
"parameter_bindings": {
"runtime": "external_callable"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "safe_npz_reference_cnn_runtime",
"parameter_bindings": {
"runtime": "learned_checkpoint"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "external_model_decoder_slot",
"notes": "The recipe supplies the typed decoder boundary; the researcher supplies the model architecture and training method.",
"parameter_bindings": {
"runtime": "training_interface"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.deepjscc_external_encode`
**Name:** DeepJSCC encoder replacement / returned-artifact slot
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `` | Entrypoint in the registered artifact manifest; defaults to encoder or decoder by slot role. |
| `artifact_manifest_path` | `string` | no | default `` | Registered trained-artifact manifest implementing this DeepJSCC slot. |
| `artifact_package_sha256` | `string` | no | default `` | Required package identity of the frozen trained artifact. |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `checkpoint_format` | `string` | no | default `noema_deepjscc_reference_cnn_npz_v1`
values `noema_deepjscc_reference_cnn_npz_v1` | |
| `checkpoint_max_bytes` | `integer` | no | default `67108864` | |
| `checkpoint_path` | `string` | no | default `` | Managed safe-NPZ DeepJSCC checkpoint used by runtime=learned_checkpoint. |
| `checkpoint_sha256` | `string` | no | default `` | Required lowercase SHA-256 of the frozen DeepJSCC checkpoint. |
| `checkpoint_strict` | `boolean` | no | default `True` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
| `runtime` | `string` | no | default `training_interface`
values `training_interface`, `external_callable`, `learned_checkpoint`, `learned_artifact` | Declare an export-only interface, invoke a trusted callable, use the reference checkpoint runtime, or run a registered portable trained artifact. |
| `symbol_channels` | `integer` | no | default `16` | Complex channel count declared by the managed checkpoint. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
This operation defines a portable encoder replacement boundary. When selected, its current implementation is omitted and the researcher-supplied module owns parameters and autograd behavior.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "encoder",
"artifact_manifest_path": "trained_artifact.yaml",
"runtime": "learned_artifact"
},
"component_id": "encoder",
"component_role": "encoder",
"entrypoint_id": "encoder",
"inputs": {
"images": {
"dtype": "float32",
"shape": [
"batch",
3,
"height",
"width"
]
}
},
"outputs": {
"symbols_ri": {
"dtype": "float32",
"shape": [
"batch",
"real_imag_channel",
"symbol_height",
"symbol_width"
]
}
},
"required_operation_inputs": [
"images"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `external`, `torch`, `onnxruntime` |
| `dataset_capture` | `external`, `torch`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "external",
"implementation": "external_callable_runtime",
"parameter_bindings": {
"runtime": "external_callable"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "safe_npz_reference_cnn_runtime",
"parameter_bindings": {
"runtime": "learned_checkpoint"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "external",
"implementation": "external_callable_runtime",
"parameter_bindings": {
"runtime": "external_callable"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "safe_npz_reference_cnn_runtime",
"parameter_bindings": {
"runtime": "learned_checkpoint"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"runtime": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "external_model_encoder_slot",
"notes": "The recipe supplies the typed encoder boundary; the researcher supplies the model architecture and training method.",
"parameter_bindings": {
"runtime": "training_interface"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.diffusers_autoencoderkl_decode`
**Name:** Diffusers AutoencoderKL continuous latents decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `model_id` | `string` | no | default `stabilityai/sd-vae-ft-mse` | Matched to the encoder model id by the dashboard. |
| `revision` | `string` | no | default `` | Full 40-character model-repository commit; required for remote models and matched to encoder evidence. |
| `scale_latents` | `boolean` | no | default `True` | Inverse of the encoder latent scaling. |
| `scaling_factor` | `number` | no | default `0.18215` | Matched to the encoder scaling factor by the dashboard. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.diffusers_autoencoderkl_encode`
**Name:** Diffusers AutoencoderKL image encoder to continuous latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `model_id` | `string` | no | default `stabilityai/sd-vae-ft-mse` | Diffusers AutoencoderKL repository id used by both encoder and decoder. |
| `revision` | `string` | no | default `` | Full 40-character model-repository commit; required for remote models and matched by the decoder. |
| `sample_mode` | `string` | no | default `mean`
values `mean`, `sample` | mean is deterministic; sample draws from the VAE latent distribution and can vary between runs. |
| `scale_latents` | `boolean` | no | default `True` | Applies the Stable Diffusion latent scale before transmission; decoder applies the inverse. |
| `scaling_factor` | `number` | no | default `0.18215` | Latent scale factor shared by encoder and decoder. Keep this matched to the model config. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.diffusers_vqmodel_decode`
**Name:** Diffusers VQModel quantized semantic latents decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `model_id` | `string` | no | default `CompVis/ldm-celebahq-256` | |
| `revision` | `string` | no | default `` | Full 40-character model-repository commit; required for remote models and matched to encoder evidence. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.diffusers_vqmodel_encode`
**Name:** Diffusers VQModel image encoder to quantized semantic latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `model_id` | `string` | no | default `CompVis/ldm-celebahq-256` | |
| `revision` | `string` | no | default `` | Full 40-character model-repository commit; required for remote models and matched by the decoder. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.eflic_aoti_decode`
**Name:** EF-LIC AOT Inductor fixed-length payload bits decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "NumPy bit unpacking + AOT Inductor EF-LIC decompress wrapper",
"language": "Python/NumPy + PyTorch AOT Inductor",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_aoti_decode_indices`
**Name:** EF-LIC AOT Inductor decoder from VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "AOT Inductor EF-LIC decompress wrapper",
"language": "PyTorch AOT Inductor",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_aoti_encode`
**Name:** EF-LIC AOT Inductor encoder to fixed-length payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "AOT Inductor EF-LIC compress wrapper + NumPy bit packing",
"language": "PyTorch AOT Inductor + Python/NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_aoti_encode_indices`
**Name:** EF-LIC AOT Inductor encoder to VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.aot_inductor.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `cpu` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "AOT Inductor EF-LIC compress wrapper",
"language": "PyTorch AOT Inductor",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_bits_to_indices`
**Name:** EF-LIC payload decoder from bits to VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_decode`
**Name:** EF-LIC fixed-length payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_decode_indices`
**Name:** EF-LIC decoder from VQ/RVQ indices to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_encode`
**Name:** EF-LIC encoder to fixed-length payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_encode_indices`
**Name:** EF-LIC encoder to VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_export_aoti`
**Name:** Compile EF-LIC fixed-shape wrappers with AOT Inductor
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `model` | `model.aot_inductor.bundle` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
| `validate_export` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.eflic_export_onnx`
**Name:** Export EF-LIC fixed-shape ONNX Runtime bundle
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `model` | `model.onnx.bundle` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to downloading EF_LIC.py. An expected_model_sha256 is mandatory when enabled. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/eflic/checkpoint.pth.tar` | Local path to the pretrained EF-LIC checkpoint.pth.tar file. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1XrfmdUx0nFFBg9_ToVzz-A2jFZ5qiGR6/view?usp=sharing` | Official checkpoint page. Browser download is often more reliable than automated Google Drive download. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Optional expected SHA-256 for the local EF-LIC checkpoint; release recipes must supply it. |
| `expected_model_sha256` | `string` | no | | Required SHA-256 for EF_LIC.py when auto setup is enabled; also verifies an existing local file when supplied. |
| `force_ind` | `integer` | no | default `2` | EF-LIC rate point. The official inference script evaluates force_ind values 0, 1, 2, 3, and 4. |
| `model_url` | `string` | no | default `https://raw.githubusercontent.com/SevenCTHU/EF-LIC/main/EF_LIC.py` | Raw EF_LIC.py URL used when auto setup is enabled and repo_path is missing the file. |
| `opset` | `integer` | no | default `18` | |
| `pad_to_multiple` | `integer` | no | default `64` | EF-LIC pads inputs to multiples of 64 using replicate padding. |
| `repo_path` | `string` | no | default `.noema/upstreams/EF-LIC` | Local path containing the official EF_LIC.py inference file. |
| `validate_export` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.eflic_indices_to_bits`
**Name:** EF-LIC payload encoder from VQ/RVQ indices to bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "EF-LIC official inference model + NumPy bit packing",
"language": "Python/PyTorch + NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_onnx_decode`
**Name:** EF-LIC ONNX Runtime fixed-length payload bits decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "NumPy bit unpacking + ONNX Runtime EF-LIC decompress wrapper",
"language": "Python/NumPy + ONNX Runtime",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_onnx_decode_indices`
**Name:** EF-LIC ONNX Runtime decoder from VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "ONNX Runtime EF-LIC decompress wrapper",
"language": "ONNX Runtime",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_onnx_encode`
**Name:** EF-LIC ONNX Runtime encoder to fixed-length payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "ONNX Runtime EF-LIC compress wrapper + NumPy bit packing",
"language": "ONNX Runtime + Python/NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_onnx_encode_indices`
**Name:** EF-LIC ONNX Runtime encoder to VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "ONNX Runtime EF-LIC compress wrapper",
"language": "ONNX Runtime",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_openvino_decode`
**Name:** EF-LIC OpenVINO fixed-length payload bits decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `CPU` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "NumPy bit unpacking + OpenVINO EF-LIC decompress wrapper",
"language": "Python/NumPy + OpenVINO",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_openvino_decode_indices`
**Name:** EF-LIC OpenVINO decoder from VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `CPU` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "OpenVINO EF-LIC decompress wrapper",
"language": "OpenVINO",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_openvino_encode`
**Name:** EF-LIC OpenVINO encoder to fixed-length payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `CPU` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "OpenVINO EF-LIC compress wrapper + NumPy bit packing",
"language": "OpenVINO + Python/NumPy",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.eflic_openvino_encode_indices`
**Name:** EF-LIC OpenVINO encoder to VQ/RVQ indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `device` | `string` | no | default `CPU` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "fixed-length VQ/RVQ index packing",
"coder": "none",
"implementation": "OpenVINO EF-LIC compress wrapper",
"language": "OpenVINO",
"note": "EF-LIC removes entropy coding; transmitted bits are fixed-length packed VQ/RVQ indices selected by force_ind."
}
```
### `model.equal_power_allocator`
**Name:** Equal-power allocation baseline
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.resource_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.resource_allocation_decision.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.evc_decode`
**Name:** EVC upstream payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/evc/EVC_SS_MD.pth.tar` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `evc_ss_md` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://onedrive.live.com/download?cid=2866592D5C55DF8C&resid=2866592D5C55DF8C%211231&authkey=ANrIn85RgtBH2wM` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/DCVC` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/DCVC/DCVC-family/EVC` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/microsoft/DCVC.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range Asymmetric Numeral System",
"coder": "rANS",
"implementation": "EVC MLCodec_rans pybind11 extension",
"language": "C++ extension + Python/PyTorch",
"note": "EVC's upstream EntropyCoder wraps MLCodec_rans.RansEncoder/RansDecoder."
}
```
### `model.evc_encode`
**Name:** EVC upstream checkpoint encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/evc/EVC_SS_MD.pth.tar` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `evc_ss_md` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://onedrive.live.com/download?cid=2866592D5C55DF8C&resid=2866592D5C55DF8C%211231&authkey=ANrIn85RgtBH2wM` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `ec_thread` | `boolean` | no | default `False` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `model_name` | `string` | no | default `EVC_SS`
values `EVC_LL`, `EVC_ML`, `EVC_SL`, `EVC_LM`, `EVC_LS`, `EVC_MM`, `EVC_SS`, `Scale_EVC_SL`, `Scale_EVC_SS` | |
| `rate_idx` | `integer` | no | default `0` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/DCVC` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/DCVC/DCVC-family/EVC` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/microsoft/DCVC.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range Asymmetric Numeral System",
"coder": "rANS",
"implementation": "EVC MLCodec_rans pybind11 extension",
"language": "C++ extension + Python/PyTorch",
"note": "EVC's upstream EntropyCoder wraps MLCodec_rans.RansEncoder/RansDecoder."
}
```
### `model.evc_export_onnx`
**Name:** Convert EVC neural transforms to ONNX
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `model` | `model.onnx.bundle` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/evc/EVC_SS_MD.pth.tar` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `evc_ss_md` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://onedrive.live.com/download?cid=2866592D5C55DF8C&resid=2866592D5C55DF8C%211231&authkey=ANrIn85RgtBH2wM` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `ec_thread` | `boolean` | no | default `False` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `export_height` | `integer` | no | default `64` | |
| `export_width` | `integer` | no | default `64` | |
| `model_name` | `string` | no | default `EVC_SS`
values `EVC_LL`, `EVC_ML`, `EVC_SL`, `EVC_LM`, `EVC_LS`, `EVC_MM`, `EVC_SS`, `Scale_EVC_SL`, `Scale_EVC_SS` | |
| `opset` | `integer` | no | default `18` | |
| `rate_idx` | `integer` | no | default `0` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/DCVC` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/DCVC/DCVC-family/EVC` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/microsoft/DCVC.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
| `validate_export` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range Asymmetric Numeral System",
"coder": "rANS",
"implementation": "EVC MLCodec_rans pybind11 extension",
"language": "C++ extension + Python/PyTorch",
"note": "EVC's upstream EntropyCoder wraps MLCodec_rans.RansEncoder/RansDecoder."
}
```
### `model.evc_onnx_decode`
**Name:** EVC ONNX Runtime payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range Asymmetric Numeral System",
"coder": "rANS",
"implementation": "ONNX Runtime EVC neural transforms + EVC MLCodec_rans entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "EVC's upstream EntropyCoder wraps MLCodec_rans.RansEncoder/RansDecoder."
}
```
### `model.evc_onnx_encode`
**Name:** EVC ONNX Runtime encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `model` | `model.onnx.bundle` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `provider` | `string` | no | default `CPUExecutionProvider` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "range Asymmetric Numeral System",
"coder": "rANS",
"implementation": "ONNX Runtime EVC neural transforms + EVC MLCodec_rans entropy backend",
"language": "ONNX Runtime + C++ extension + Python/PyTorch",
"note": "EVC's upstream EntropyCoder wraps MLCodec_rans.RansEncoder/RansDecoder."
}
```
### `model.external_decode_bits`
**Name:** External payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `bit_order` | `string` | no | default `big`
values `big`, `little` | Bit order used when converting packed bytes to and from the channel bit vector. |
| `bit_storage` | `string` | no | default `unpacked_bits`
values `unpacked_bits`, `packed_bytes` | unpacked_bits means one array element is one channel bit. packed_bytes means the callable returns bytes that Noema expands with np.unpackbits before channel transmission. |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.external_decode_indices`
**Name:** External semantic indices decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.external_decode_latents`
**Name:** External continuous semantic latents decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.external_encode_bits`
**Name:** External image encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `bit_order` | `string` | no | default `big`
values `big`, `little` | Bit order used when converting packed bytes to and from the channel bit vector. |
| `bit_storage` | `string` | no | default `unpacked_bits`
values `unpacked_bits`, `packed_bytes` | unpacked_bits means one array element is one channel bit. packed_bytes means the callable returns bytes that Noema expands with np.unpackbits before channel transmission. |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.external_encode_indices`
**Name:** External image encoder to semantic indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.external_encode_latents`
**Name:** External image encoder to continuous semantic latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `array_params`
values `array_params`, `array`, `dict` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.hpcm_decode`
**Name:** HPCM upstream payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/hpcm/hpcm_base_lambda0.013_mse.pth` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `hpcm_base_l0.013_mse` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1Snq7vkWQdApzCe-gK_V-WuRyMHQRL443/view?usp=drive_link` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/LIC-HPCM` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/LIC-HPCM` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/lyq133/LIC-HPCM.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "unbounded range Asymmetric Numeral System",
"coder": "unbounded rANS",
"implementation": "LIC-HPCM unbounded_ans pybind11 extension",
"language": "C++ extension + Python/PyTorch",
"note": "HPCM uses its upstream unbounded_rans arithmetic coder for real bitstream writing."
}
```
### `model.hpcm_encode`
**Name:** HPCM upstream checkpoint encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/hpcm/hpcm_base_lambda0.013_mse.pth` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `hpcm_base_l0.013_mse` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1Snq7vkWQdApzCe-gK_V-WuRyMHQRL443/view?usp=drive_link` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `model_name` | `string` | no | default `HPCM_Base`
values `HPCM_Base`, `HPCM_Large`, `HPCM_Base_PhiContext`, `HPCM_1B` | |
| `pad_to_multiple` | `integer` | no | default `256` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/LIC-HPCM` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/LIC-HPCM` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/lyq133/LIC-HPCM.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `scale_table_levels` | `integer` | no | default `60` | |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "unbounded range Asymmetric Numeral System",
"coder": "unbounded rANS",
"implementation": "LIC-HPCM unbounded_ans pybind11 extension",
"language": "C++ extension + Python/PyTorch",
"note": "HPCM uses its upstream unbounded_rans arithmetic coder for real bitstream writing."
}
```
### `model.isac_ofdm_allocator_adapter`
**Name:** ISAC OFDM allocation adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `isac.ofdm_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `isac.ofdm_power_allocation.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `isac_allocator` | |
| `artifact_manifest_path` | `string` | no | default `` | |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `equal_power`
values `equal_power`, `communications_water_filling`, `scalarized_reference`, `learned_artifact` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
The allocation boundary supports a portable power-simplex policy artifact.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "isac_allocator",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "allocator",
"component_role": "joint_isac_ofdm_power_allocator",
"entrypoint_id": "isac_allocator",
"inputs": {
"features": {
"dtype": "float32",
"shape": [
"batch",
"subcarrier",
4
]
}
},
"outputs": {
"power": {
"dtype": "float32",
"shape": [
"batch",
"subcarrier"
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "equal_power",
"parameter_bindings": {
"mode": "equal_power"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "communication_water_filling",
"parameter_bindings": {
"mode": "communications_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "scalarized_projected_gradient_oracle",
"parameter_bindings": {
"mode": "scalarized_reference"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "reference_allocators",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "label_free_scalarized_utility",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.jpeg_decode`
**Name:** JPEG payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros`, `gray_image`, `erasure`, `report_outage` | Use non-fail policies only when intentionally studying protected-link outages or corrupted entropy-coded payloads. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Classical JPEG parsing, dequantization, and entropy decoding are not exposed as a differentiable training block.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "JPEG Huffman coding",
"coder": "Huffman",
"implementation": "Pillow JPEG backend",
"language": "C/Python",
"note": "Baseline JPEG comparisons normally use non-progressive JPEG without optimized Huffman-table search."
}
```
### `model.jpeg_encode`
**Name:** JPEG image encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `optimize` | `boolean` | no | default `False` | Advanced. Extra entropy-coding pass for smaller files at the same quantization; leave off for a plain baseline JPEG comparison unless you report optimized JPEG. |
| `progressive` | `boolean` | no | default `False` | Advanced. Store the JPEG in progressive scan order for streaming/preview; leave off for the usual baseline sequential JPEG comparison unless explicitly studying progressive JPEG. |
| `quality` | `integer` | no | default `75` | JPEG quality factor. Higher values keep more detail and use more bits. |
| `subsampling` | `string` | no | default `420`
values `keep`, `444`, `422`, `420` | Chroma subsampling. 444 preserves chroma best; 420 is the common higher-compression setting. |
| `wire_format` | `string` | no | default `safe_json_base64`
values `safe_json_base64`, `compact_binary_v1` | Artifact-safe JSON/base64 remains the compatibility default; compact_binary_v1 is the bounded communication wire format for transmitted-resource studies. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Classical JPEG quantization and entropy coding are non-differentiable in Noema benchmark execution.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "JPEG Huffman coding",
"coder": "Huffman",
"implementation": "Pillow JPEG backend",
"language": "C/Python",
"note": "Baseline JPEG comparisons normally use non-progressive JPEG without optimized Huffman-table search."
}
```
### `model.leo_ntn_tracking_adapter`
**Name:** LEO-NTN Doppler and beam-handover adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ntn.tracking_history.numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `truth` | `ntn.future_state_truth.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ntn.future_state_decision.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `ntn_tracker` | |
| `artifact_manifest_path` | `string` | no | default `` | |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `linear_extrapolation`
values `hold_last`, `linear_extrapolation`, `oracle_future`, `learned_artifact` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
A bounded history tensor feeds a portable joint Doppler/beam predictor.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "ntn_tracker",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "tracker",
"component_role": "leo_ntn_doppler_beam_tracker",
"entrypoint_id": "ntn_tracker",
"inputs": {
"track_features": {
"dtype": "float32",
"shape": [
"batch",
"history",
3
]
}
},
"outputs": {
"decision": {
"dtype": "float32",
"shape": [
"batch",
"decision"
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "hold_last_observation",
"parameter_bindings": {
"mode": "hold_last"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_extrapolation",
"parameter_bindings": {
"mode": "linear_extrapolation"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "simulation_future_state",
"parameter_bindings": {
"mode": "oracle_future"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "reference_trackers",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "supervised_future_state_prediction",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.localization_adapter`
**Name:** Localization/sensing adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.localization_problem.numpy`, `ai_phy.localization_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.localization_estimate.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `localization_estimator` | |
| `artifact_manifest_path` | `string` | no | default `` | Registered schema-v2 trained artifact implementing the range-localization ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `regularized_trilateration`
values `trilateration`, `regularized_trilateration`, `learned_artifact` | Select a fixed trilateration method or a returned portable localizer. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The adapter accepts a portable learned range localizer while linear and centroid-regularized trilateration remain fixed baselines.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "localization_estimator",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "localizer",
"component_role": "two_dimensional_range_localizer",
"entrypoint_id": "localization_estimator",
"inputs": {
"anchors": {
"dtype": "float32",
"shape": [
"batch",
"anchor",
2
]
},
"ranges": {
"dtype": "float32",
"shape": [
"batch",
"anchor"
]
}
},
"outputs": {
"positions": {
"dtype": "float32",
"shape": [
"batch",
2
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "linear_trilateration",
"parameter_bindings": {
"mode": "trilateration"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "centroid_regularized_trilateration",
"parameter_bindings": {
"mode": "regularized_trilateration"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "linear_trilateration",
"parameter_bindings": {
"mode": "trilateration"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "centroid_regularized_trilateration",
"parameter_bindings": {
"mode": "regularized_trilateration"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_localization_endpoint",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_rt_localization_endpoint",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `model.ls_channel_estimator`
**Name:** LS channel estimator baseline
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.channel_estimation_problem.numpy`, `ai_phy.channel_estimation_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.channel_estimate.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Least-squares benchmark baseline is an artifact operation.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"tolerance": {
"atol": 1e-07,
"rtol": 1e-06
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.modulation_classifier_adapter`
**Name:** Modulation classifier
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `observation` | `ai_phy.modulation_iq_frames.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `prediction` | `ai_phy.modulation_predictions.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `modulation_classifier` | |
| `artifact_manifest_path` | `string` | no | default `` | Schema-v2 trained artifact implementing the modulation-classifier ABI. |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `classical_cumulant`
values `classical_cumulant`, `classical_likelihood`, `classical_evm`, `learned_artifact` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The classifier slot accepts a portable learned artifact while classical modes remain reproducible baselines.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "modulation_classifier",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "classifier",
"component_role": "modulation_classifier",
"entrypoint_id": "modulation_classifier",
"inputs": {
"iq_ri": {
"dtype": "float32",
"shape": [
"batch",
"sample",
2
]
}
},
"outputs": {
"class_logits": {
"dtype": "float32",
"shape": [
"batch",
3
]
}
},
"required_operation_inputs": [
"observation"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Classifiers are compared through the fixed class vocabulary and task metrics.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "onnx",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "blind_differential_cumulant",
"parameter_bindings": {
"mode": "classical_cumulant"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "awgn_mixture_likelihood",
"parameter_bindings": {
"mode": "classical_likelihood"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "minimum_constellation_evm",
"parameter_bindings": {
"mode": "classical_evm"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "blind_differential_cumulant",
"parameter_bindings": {
"mode": "classical_cumulant"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "awgn_mixture_likelihood",
"parameter_bindings": {
"mode": "classical_likelihood"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "modulation_classification_training_endpoint",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.mrt_beamformer`
**Name:** MRT/codebook beamformer baseline
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.beamforming_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.beamforming_decision.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.music_aoa_estimator`
**Name:** MUSIC AoA estimator baseline
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.aoa_problem.numpy`, `ai_phy.aoa_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.aoa_estimate.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `grid_size` | `integer` | no | default `721` | |
| `grid_step_deg` | `number` | no | | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Classical sample-covariance eigendecomposition and grid search benchmark.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.near_field_estimator_adapter`
**Name:** Near-field range-angle estimator adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `near_field.array_observation.numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `truth` | `near_field.range_angle_truth.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `near_field.range_angle_estimate.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `artifact_entrypoint` | `string` | no | default `near_field_estimator` | |
| `artifact_manifest_path` | `string` | no | default `` | |
| `artifact_package_sha256` | `string` | no | default `` | |
| `mode` | `string` | no | default `polar_codebook`
values `far_field_steering`, `polar_codebook`, `oracle_focus`, `learned_artifact` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
The coherent array observation is exposed through a portable range-angle estimator ABI.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "near_field_estimator",
"artifact_manifest_path": "trained_artifact.yaml",
"mode": "learned_artifact"
},
"component_id": "estimator",
"component_role": "near_field_range_angle_estimator",
"entrypoint_id": "near_field_estimator",
"inputs": {
"array_ri": {
"dtype": "float32",
"shape": [
"batch",
"antenna",
2
]
}
},
"outputs": {
"range_angle": {
"dtype": "float32",
"shape": [
"batch",
2
]
}
},
"required_operation_inputs": [
"problem"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "far_field_angle_grid",
"parameter_bindings": {
"mode": "far_field_steering"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "polar_range_angle_codebook",
"parameter_bindings": {
"mode": "polar_codebook"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "simulation_truth",
"parameter_bindings": {
"mode": "oracle_focus"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_runtime",
"parameter_bindings": {
"mode": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "reference_estimators",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "supervised_range_angle_estimation",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.resource_allocation_adapter`
**Name:** Resource allocation adapter
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.resource_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.resource_allocation_decision.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `temperature` | `number` | no | default `1.0` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`True`, exportable=`True`
Surrogate-gradient metadata describes this adapter only when retained as downstream support. Portable replacement is not available until a trained-artifact ABI and runtime binding are implemented.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "weighted_gain_softmax_reference",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "weighted_gain_softmax_reference",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_resource_policy_endpoint",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.symbol_power_allocator`
**Name:** Budgeted symbol power allocator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
| `channel_state` | `channel.ofdm_channel_state.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `allocation` | `channel.power_allocation.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `allocation_contrast` | `number` | no | default `0.6` | |
| `allocation_lower_power_ratio` | `number` | no | default `0.1` | Minimum per-subcarrier power relative to the mean budget for bounded causal-AR water filling. |
| `allocation_upper_power_ratio` | `number` | no | default `1.9` | Maximum per-subcarrier power relative to the mean budget for bounded causal-AR water filling. |
| `artifact_entrypoint` | `string` | no | default `power_policy` | Entrypoint in the registered artifact manifest used for allocator inference. |
| `artifact_manifest_path` | `string` | no | default `` | Registered trained-artifact manifest implementing the architecture-neutral allocator slot ABI. |
| `artifact_package_sha256` | `string` | no | default `` | Registry-independent digest of the complete trained-artifact package bound into the execution plan. |
| `bit_loading_bpsk_min_snr_db` | `number` | no | default `6.0` | |
| `bit_loading_max_bits_per_symbol` | `integer` | no | default `6`
values `1`, `2`, `4`, `6` | |
| `bit_loading_qam16_min_snr_db` | `number` | no | default `17.0` | |
| `bit_loading_qam64_min_snr_db` | `number` | no | default `23.0` | |
| `bit_loading_qpsk_min_snr_db` | `number` | no | default `10.0` | |
| `budget_mode` | `string` | no | default `fixed_average`
values `fixed_average`, `variable_average` | |
| `checkpoint_format` | `string` | no | default `noema_csi_power_deepset_npz_v1`
values `noema_csi_power_deepset_npz_v1` | |
| `checkpoint_max_bytes` | `integer` | no | default `67108864` | |
| `checkpoint_path` | `string` | no | default `` | Frozen safe-NPZ CSI allocator checkpoint used by policy=learned_checkpoint. |
| `checkpoint_sha256` | `string` | no | default `` | Required lowercase SHA-256 of the frozen allocator checkpoint. |
| `checkpoint_strict` | `boolean` | no | default `True` | |
| `csi_gain_shrinkage` | `number` | no | default `0.6` | For uncertainty-shrunk water filling, blend observed gains toward their per-state mean before allocating power. |
| `csi_prediction_gain_confidence` | `number` | no | default `0.4` | Blend complex-AR predicted gains toward their per-state frequency mean before water filling. |
| `csi_prediction_horizon_ofdm_symbols` | `integer` | no | default `0` | For complex-AR prediction, forecast this many OFDM symbols past the newest causal CSI snapshot. Zero uses the CSI artifact's declared feedback delay. |
| `eps` | `number` | no | default `1e-12` | |
| `granularity` | `string` | no | default `global`
values `global`, `per_symbol`, `per_subcarrier`, `per_stream` | |
| `label` | `string` | no | default `tx_power_allocate` | |
| `max_power` | `number` | no | default `2.0` | |
| `midpoint_snr_db` | `number` | no | default `12.0` | |
| `min_power` | `number` | no | default `0.25` | |
| `model_batch_size` | `integer` | no | default `1024` | Maximum number of independent channel-state examples evaluated in one learned-model inference call. Chunking preserves the complete experiment batch and output order. |
| `policy` | `string` | no | default `snr_sigmoid`
values `fixed`, `snr_sigmoid`, `water_filling`, `observed_csi_water_filling`, `robust_csi_water_filling`, `causal_ar_water_filling`, `causal_ar_box_water_filling`, `learned_checkpoint`, `learned_artifact` | Power-allocation method. Model selection is shown only for learned policies. |
| `power_unit` | `string` | no | default `normalized`
values `normalized`, `mW` | |
| `slope_db` | `number` | no | default `4.0` | |
| `snr_db` | `number` | no | default `12.0` | Fallback reference SNR used only by the SNR-adaptive sigmoid policy when no explicit channel state is connected. |
| `stream_count` | `integer` | no | default `1` | |
| `subcarrier_count` | `integer` | no | default `64` | |
| `target_power` | `number` | no | default `1.0` | Normalized average complex-symbol power budget per resource element; the per-state sum constraint is this value times the subcarrier count. |
| `transport_mode` | `string` | no | default `fixed_modulation`
values `fixed_modulation`, `allocation_aware_bit_loading` | Optionally convert the coded-bit stream into a CSI/allocation-aware OFDM modulation map. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
Continuous-symbol power allocation is differentiable. Noema can benchmark a frozen, CSI-conditioned artifact behind an architecture-neutral runtime ABI while model training remains outside the benchmark executor.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`True`
**Portable trained-artifact ABI:**
```json
{
"binding_params": {
"artifact_entrypoint": "power_policy",
"artifact_manifest_path": "trained_artifact.yaml",
"budget_mode": "fixed_average",
"granularity": "per_subcarrier",
"policy": "learned_artifact"
},
"component_id": "policy",
"component_role": "power_policy",
"constraint_adapter": "noema_exact_simplex_projection_v1",
"entrypoint_id": "power_policy",
"inputs": {
"average_power_budget": {
"dtype": "float32",
"shape": [
"batch",
1
]
},
"channel_gain": {
"dtype": "float32",
"shape": [
"batch",
"subcarrier"
]
},
"noise_variance": {
"dtype": "float32",
"shape": [
"batch",
1
]
}
},
"outputs": {
"allocation_scores": {
"dtype": "float32",
"shape": [
"batch",
"subcarrier"
]
}
},
"required_operation_inputs": [
"channel_state"
]
}
```
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `onnxruntime` |
| `dataset_capture` | `numpy`, `onnxruntime` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Reference NumPy and Torch policies should select the same group power targets and scaled symbols for fixed parameters.",
"tolerance": {
"atol": 1e-06,
"rtol": 1e-05
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"checkpoint": "noema_csi_power_deepset_npz_v1",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "fixed_power_allocator",
"parameter_bindings": {
"policy": "fixed"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "snr_sigmoid_power_allocator",
"parameter_bindings": {
"policy": "snr_sigmoid"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "closed_form_water_filling_allocator",
"parameter_bindings": {
"policy": "water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "water_filling_on_observed_csi",
"parameter_bindings": {
"policy": "observed_csi_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "correlation_shrunk_water_filling",
"parameter_bindings": {
"policy": "robust_csi_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_water_filling",
"parameter_bindings": {
"policy": "causal_ar_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_box_constrained_water_filling",
"parameter_bindings": {
"policy": "causal_ar_box_water_filling"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_policy_plus_noema_projection",
"parameter_bindings": {
"policy": "learned_artifact"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "fixed_power_allocator",
"parameter_bindings": {
"policy": "fixed"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "snr_sigmoid_power_allocator",
"parameter_bindings": {
"policy": "snr_sigmoid"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "closed_form_water_filling_allocator",
"parameter_bindings": {
"policy": "water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "water_filling_on_observed_csi",
"parameter_bindings": {
"policy": "observed_csi_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "correlation_shrunk_water_filling",
"parameter_bindings": {
"policy": "robust_csi_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_water_filling",
"parameter_bindings": {
"policy": "causal_ar_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_complex_ar_prediction_plus_box_constrained_water_filling",
"parameter_bindings": {
"policy": "causal_ar_box_water_filling"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "onnxruntime",
"implementation": "portable_trained_artifact_policy_plus_noema_projection",
"parameter_bindings": {
"policy": "learned_artifact"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "trainable_budgeted_symbol_allocator",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "safe_npz_csi_deepset_checkpoint",
"parameter_bindings": {
"policy": "learned_checkpoint"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "safe_npz_csi_deepset_checkpoint",
"parameter_bindings": {
"policy": "learned_checkpoint"
},
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.tcm_decode`
**Name:** TCM upstream payload bits decoder to image batch
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/tcm/tcm_N128_lambda0.05_mse.pth` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `tcm_n128_l0.05_mse` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1TK-CPiD2QwtWJqZoT_OyCtnxdQ7UNP56/view?usp=share_link` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `on_error` | `string` | no | default `fail`
values `fail`, `zeros` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/LIC_TCM` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/LIC_TCM` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/jmliu206/LIC_TCM.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "rANS",
"coder": "CompressAI BufferedRansEncoder/RansDecoder",
"implementation": "CompressAI ANS backend used by LIC_TCM",
"language": "C++ extension + Python/PyTorch",
"note": "TCM's upstream model imports compressai.ans.BufferedRansEncoder and RansDecoder for bitstream coding."
}
```
### `model.tcm_encode`
**Name:** TCM upstream checkpoint encoder to payload bits
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `M` | `integer` | no | default `320` | |
| `N` | `integer` | no | default `128` | |
| `auto_setup` | `boolean` | no | default `False` | Opt in to fetching pinned upstream assets. repo_revision and expected_checkpoint_sha256 are mandatory when downloads are needed. |
| `checkpoint` | `string` | no | default `.noema/checkpoints/tcm/tcm_N128_lambda0.05_mse.pth` | Local path to the pretrained checkpoint for this RD point. |
| `checkpoint_preset` | `string` | no | default `tcm_n128_l0.05_mse` | Dashboard preset identifier for the selected upstream checkpoint. |
| `checkpoint_url` | `string` | no | default `https://drive.google.com/file/d/1TK-CPiD2QwtWJqZoT_OyCtnxdQ7UNP56/view?usp=share_link` | Pretrained checkpoint URL used when auto setup is enabled. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `device` | `string` | no | default `cpu` | |
| `expected_checkpoint_sha256` | `string` | no | | Expected checkpoint SHA-256 required for automatic checkpoint download. |
| `pad_to_multiple` | `integer` | no | default `128` | |
| `repo_clone_path` | `string` | no | default `.noema/upstreams/LIC_TCM` | Local clone target. For EVC this is the DCVC root; repo_path points to DCVC-family/EVC inside it. |
| `repo_path` | `string` | no | default `.noema/upstreams/LIC_TCM` | Local path to the official upstream repository clone. |
| `repo_revision` | `string` | no | | Immutable 40-character Git commit required for automatic repository setup. |
| `repo_url` | `string` | no | default `https://github.com/jmliu206/LIC_TCM.git` | Official upstream Git repository URL used when auto setup is enabled. |
| `setup_timeout_s` | `integer` | no | default `900` | Maximum time for each upstream auto-setup clone/download operation. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
**Entropy/payload info:**
```json
{
"algorithm": "rANS",
"coder": "CompressAI BufferedRansEncoder/RansDecoder",
"implementation": "CompressAI ANS backend used by LIC_TCM",
"language": "C++ extension + Python/PyTorch",
"note": "TCM's upstream model imports compressai.ans.BufferedRansEncoder and RansDecoder for bitstream coding."
}
```
### `model.text_bart_jscc_decode`
**Name:** BART JSCC-lite text semantic decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy`, `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `cache_dir` | `string` | no | default `` | |
| `device` | `string` | no | default `cpu` | |
| `generation_max_length` | `integer` | no | default `64` | |
| `model_id` | `string` | no | default `facebook/bart-base` | |
| `model_revision` | `string` | no | default `aadd2ab0ae0c8268c7c9693540e9904811f36177` | Full immutable Hugging Face commit; required for remote models and matched to sender evidence. |
| `num_beams` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
The semantic decoder is a PyTorch/Transformers module; benchmark execution uses pretrained eval mode.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `torch` |
| `dataset_capture` | `torch` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "torch",
"implementation": "transformers_eval_artifact",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "transformers_capture_artifact",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "transformers_decoder_module",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.text_bart_jscc_encode`
**Name:** BART JSCC-lite text semantic encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `cache_dir` | `string` | no | default `` | |
| `device` | `string` | no | default `cpu` | |
| `max_length` | `integer` | no | default `64` | |
| `model_id` | `string` | no | default `facebook/bart-base` | |
| `model_revision` | `string` | no | default `aadd2ab0ae0c8268c7c9693540e9904811f36177` | Full immutable Hugging Face commit; required for remote models. |
| `power_normalize` | `boolean` | no | default `True` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`True`, exportable=`True`
The semantic encoder is a PyTorch/Transformers module; benchmark execution uses pretrained eval mode.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `torch` |
| `dataset_capture` | `torch` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "torch",
"implementation": "transformers_eval_artifact",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "transformers_capture_artifact",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "transformers_encoder_module",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `model.text_utf8_decode`
**Name:** Text UTF-8 payload decoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `on_error` | `string` | no | default `replace`
values `replace`, `fail` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.text_utf8_encode`
**Name:** Text UTF-8 payload encoder
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `payload_format` | `string` | no | default `json_utf8`
values `json_utf8` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.trilateration_localizer`
**Name:** Trilateration localization baseline
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.localization_problem.numpy`, `ai_phy.localization_observation.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `estimate` | `ai_phy.localization_estimate.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `model.water_filling_power_allocator`
**Name:** Theoretical water-filling power allocator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.resource_allocation_problem.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `decision` | `ai_phy.resource_allocation_decision.numpy` |
**Parameters:**
None.
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Closed-form capacity-optimal oracle for parallel Gaussian fading channels under a fixed sum-power constraint.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Allocations must satisfy the fixed sum-power constraint and the KKT water-level solution.",
"tolerance": {
"atol": 1e-06,
"rtol": 1e-05
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "closed_form_parallel_channel_water_filling",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "closed_form_parallel_channel_water_filling_labels",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## modulation
### `modulation.digital_modulate`
**Name:** Digital bit-to-symbol modulator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `modulation` | `string` | no | default `qpsk`
values `bpsk`, `qpsk`, `qam16` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Hard bit-to-constellation mapping is discrete; differentiable exports should use a differentiable modem surrogate or Sionna block.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `cpp` |
| `dataset_capture` | `numpy`, `cpp` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Hard mapper implementations should produce the same normalized constellation symbols within floating-point tolerance.",
"tolerance": {
"atol": 1e-07,
"rtol": 1e-06
},
"type": "numerical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_bpsk_modulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qpsk_modulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qam16_modulator",
"parameter_bindings": {
"modulation": "qam16"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_bpsk_modulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_qpsk_modulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_bpsk_modulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qpsk_modulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_qam16_modulator",
"parameter_bindings": {
"modulation": "qam16"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_bpsk_modulator",
"parameter_bindings": {
"modulation": "bpsk"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "cpp",
"implementation": "cpp_qpsk_modulator",
"parameter_bindings": {
"modulation": "qpsk"
},
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `modulation.identity_modulate`
**Name:** Identity PHY bit-to-symbol mapper
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `label` | `string` | no | default `identity_phy_modulator` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`surrogate`, trainable_params=`False`, exportable=`True`
Artifact execution maps discrete uint8 bits to 0/1 complex symbols. Differentiable export may materialize this as an identity over continuous logits/symbols, but gradients through hard bits are surrogate-only.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "Identity PHY modulation stores each bit as one finite complex symbol with real value 0 or 1.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `modulation.qpsk_pilot_modulate`
**Name:** QPSK pilot-frame modulator
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `pilot_context` | `channel.qpsk_pilot_context.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `pilot_seed` | `integer` | no | default `1701` | |
| `pilot_spacing_data_symbols` | `integer` | no | default `16` | |
| `preamble_symbols` | `integer` | no | default `16` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`stop`, trainable_params=`False`, exportable=`False`
Hard QPSK mapping and discrete pilot insertion are benchmark framing operations.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "The same bits, packet layout, and pilot seed define an exact framed symbol stream.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## noise
### `noise.representation_indices`
**Name:** Representation noise on discrete indices
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `indices` | `semantic.indices.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `burst_length` | `integer` | no | default `64` | |
| `mode` | `string` | no | default `none`
values `none`, `dropout`, `random_replace`, `burst` | |
| `probability` | `number` | no | default `0.0` | |
| `replacement` | `integer` | no | default `0` | |
| `seed` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `noise.representation_latents`
**Name:** Representation-domain noise on continuous latents
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `latents` | `semantic.latents.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `mode` | `string` | no | default `none`
values `none`, `gaussian`, `dropout` | |
| `probability` | `number` | no | default `0.05` | |
| `seed` | `integer` | no | default `0` | |
| `sigma` | `number` | no | default `0.05` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Artifact-mode latent perturbation is executed as NumPy preprocessing; differentiable training export needs a torch materialization.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Stochastic latent perturbations are equivalent by declared distribution and seed policy.",
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `noise.source_image_perturbation`
**Name:** Source-domain image perturbation
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `blur_radius` | `number` | no | default `1.5` | |
| `mask_value` | `number` | no | default `0.0` | |
| `mode` | `string` | no | default `none`
values `none`, `gaussian`, `blur`, `patch_mask`, `salt_pepper` | |
| `patch_size` | `integer` | no | default `32` | |
| `probability` | `number` | no | default `0.02` | |
| `seed` | `integer` | no | default `0` | |
| `sigma` | `number` | no | default `0.02` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Artifact-mode source perturbations are stochastic preprocessing blocks, not differentiable training modules.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Stochastic perturbations are equivalent by declared distribution and seed policy.",
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `noise.source_text_perturbation`
**Name:** Source semantic perturbation for text
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `mask_token` | `string` | no | default `[MASK]` | |
| `mode` | `string` | no | default `none`
values `none`, `drop_words`, `mask_words`, `shuffle_words` | |
| `probability` | `number` | no | default `0.0` | |
| `seed` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## source
### `source.ai_phy_channel_realization`
**Name:** AI-PHY channel realization source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `channel` | `ai_phy.channel_realization.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `carrier_frequency_ghz` | `number` | no | default `3.5` | |
| `channel_tap_count` | `integer` | no | default `4` | |
| `delay_spread_ns` | `number` | no | default `300.0` | |
| `example_count` | `integer` | no | default `32` | |
| `mobility_kmh` | `number` | no | default `30.0` | |
| `normalize_channel` | `boolean` | no | default `True` | |
| `rx_antennas` | `integer` | no | default `1` | |
| `scenario` | `string` | no | default `pilot_awgn`
values `pilot_awgn`, `flat_siso`, `mimo_ofdm_pilot`, `mimo_ofdm`, `mimo_ofdm_3gpp_tdl` | |
| `seed` | `integer` | no | default `0` | |
| `subcarrier_spacing_khz` | `number` | no | default `30.0` | |
| `subcarriers` | `integer` | no | default `1` | |
| `tdl_model` | `string` | no | default `C`
values `A`, `B`, `C`, `D`, `E` | |
| `tx_antennas` | `integer` | no | default `1` | |
| `wireless_backend` | `string` | no | default `auto`
values `auto`, `numpy`, `sionna` | Wireless implementation. auto resolves deterministically to NumPy; select sionna explicitly to require Sionna, with no runtime fallback. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Channel truth is a fixed-seed benchmark input, separate from the pilot observation model.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `sionna` |
| `dataset_capture` | `numpy`, `sionna` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_channel_realization",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_channel_realization",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_3gpp_tdl_ofdm_channel",
"parameter_bindings": {
"scenario": "mimo_ofdm"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_3gpp_tdl_ofdm_channel",
"parameter_bindings": {
"scenario": "mimo_ofdm"
},
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.ai_phy_pilot_channel`
**Name:** AI-PHY pilot channel scenario
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.channel_estimation_problem.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `example_count` | `integer` | no | default `32` | |
| `rx_antennas` | `integer` | no | default `1` | |
| `scenario` | `string` | no | default `pilot_awgn`
values `pilot_awgn`, `mimo_ofdm_pilot` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `12.0` | |
| `subcarriers` | `integer` | no | default `1` | |
| `tx_antennas` | `integer` | no | default `1` | |
| `wireless_backend` | `string` | no | default `auto`
values `auto`, `numpy`, `sionna` | Wireless implementation. auto resolves deterministically to NumPy; select sionna explicitly to require Sionna, with no runtime fallback. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Scenario generation is a benchmark data source; differentiable exports should materialize the channel model directly.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `sionna` |
| `dataset_capture` | `numpy`, `sionna` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "Pilot-observation backends must match the declared channel/noise distribution and SNR, not sample-identical noise.",
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_pilot_awgn",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_pilot_awgn",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_pilot_observation",
"notes": "Selected only when wireless_backend=sionna; Sionna failures are fatal.",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_pilot_observation",
"notes": "Selected only when wireless_backend=sionna; Sionna failures are fatal.",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_pilot_channel_scenario",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_pilot_channel_scenario",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.ai_phy_pilot_pattern`
**Name:** AI-PHY pilot pattern source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `pilots` | `ai_phy.pilot_pattern.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `pilot_spacing` | `integer` | no | default `4` | |
| `scenario` | `string` | no | default `unit`
values `unit`, `comb` | |
| `seed` | `integer` | no | default `0` | |
| `subcarriers` | `integer` | no | default `1` | |
| `tx_antennas` | `integer` | no | default `1` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.aoa_scene`
**Name:** Generated AoA far-field scene
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `scene` | `ai_phy.aoa_scene.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `angle_max_deg` | `number` | no | default `60.0` | Upper bound for random broadside azimuth generation. |
| `angle_min_deg` | `number` | no | default `-60.0` | Lower bound for random broadside azimuth generation. |
| `angle_mode` | `string` | no | default `random_uniform`
values `random_uniform`, `fixed` | Generate seeded random bearings or repeat one user-selected bearing. |
| `example_count` | `integer` | no | default `16` | Number of synthetic far-field source angles generated for this run. |
| `seed` | `integer` | no | default `0` | Controls repeatable random source-angle generation. |
| `source_angle_deg` | `number` | no | default `0.0` | Used only in fixed mode; 0 degrees is array broadside. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.beamforming_scenario`
**Name:** Beamforming scenario source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.beamforming_problem.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `channel_model` | `string` | no | default `iid_complex_gaussian`
values `iid_complex_gaussian`, `clustered_ula` | Use unstructured Rayleigh fading or a three-hotspot clustered ULA distribution suitable for finite-codebook design. |
| `example_count` | `integer` | no | default `32` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `10.0` | |
| `tx_antennas` | `integer` | no | default `8` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "synthetic_miso_channel_batch",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "synthetic_miso_channel_batch",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_miso_channel_batch",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_miso_channel_batch",
"notes": "Use Sionna PHY/RT channel materializations as this suite matures.",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `source.bit_manifest`
**Name:** Frozen bit payload manifest source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `item_ids` | `array` | no | default `[]` | Explicit ordered manifest item IDs. Mutually exclusive with selection. |
| `manifest_path` | `string` | yes | | Local .json/.yaml/.yml noema.bit_payload_manifest path. URI schemes and symlinks are rejected. |
| `manifest_sha256` | `string` | yes | | Expected SHA-256 of the exact manifest file bytes. |
| `selection` | `string` | no | default `` | Named manifest split. Mutually exclusive with item_ids. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Frozen discrete source bits are evidence artifacts and do not participate in gradient flow.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "A hash-pinned manifest and ordered selection must materialize the same canonical unpacked uint8 vector on every host.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.coco128_detection`
**Name:** COCO128 object-detection source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `detections` | `vision.detections.json` |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset_dir` | `string` | no | default `.noema/datasets/coco128` | |
| `download` | `boolean` | no | default `True` | |
| `image_size` | `integer` | no | default `320` | |
| `limit` | `integer` | no | default `8` | |
| `split` | `string` | no | default `train2017`
values `train2017` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.coco8_segmentation`
**Name:** COCO8 segmentation source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `segmentation` | `vision.segmentation_mask.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset_dir` | `string` | no | default `.noema/datasets/coco8-seg` | |
| `download` | `boolean` | no | default `True` | |
| `image_size` | `integer` | no | default `320` | |
| `limit` | `integer` | no | default `4` | |
| `split` | `string` | no | default `val`
values `train`, `val` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.external_classification_dataset`
**Name:** External classification dataset source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `task.predictions.json` |
| `reference` | `task.labels.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `call_style` | `string` | no | default `dict`
values `dict`, `params`, `none` | |
| `callable` | `string` | no | default `` | |
| `module` | `string` | no | default `` | |
| `path` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.flickr8k_retrieval`
**Name:** Flickr8k image-text retrieval source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `targets` | `retrieval.targets.json` |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `caption_index` | `integer` | no | default `0` | |
| `dataset_dir` | `string` | no | default `.noema/datasets/flickr8k` | |
| `download` | `boolean` | no | default `True` | |
| `image_size` | `integer` | no | default `224` | |
| `limit` | `integer` | no | default `16` | |
| `split` | `string` | no | default `validation`
values `test`, `validation` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.image_dataset`
**Name:** Real image dataset batch
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `crop_size` | `integer` | no | default `0` | |
| `dataset` | `string` | no | default `kodak` | Dataset identifier. Kodak works without a manifest; every other dataset requires a hash-pinned manifest_path. |
| `dataset_dir` | `string` | no | default `.noema/datasets/kodak` | |
| `image_ids` | `string` | no | default `kodim01,kodim02,kodim03,kodim04,kodim05,kodim06,kodim07,kodim08,kodim09,kodim10,kodim11,kodim12,kodim13,kodim14,kodim15,kodim16,kodim17,kodim18,kodim19,kodim20,kodim21,kodim22,kodim23,kodim24` | |
| `manifest_path` | `string` | no | default `` | JSON/YAML noema.image_dataset_manifest. Sample paths are confined beneath its declared root. |
| `manifest_sha256` | `string` | no | default `` | Expected lowercase SHA-256 of manifest_path; required whenever a manifest is used. |
| `repeat_count` | `integer` | no | default `1` | |
| `resize_shorter_side` | `integer` | no | default `0` | Optional aspect-preserving resize applied before the center crop; 0 keeps native resolution. |
| `split` | `string` | no | default `` | Manifest split used when image_ids is empty. |
| `training_validation_count` | `integer` | no | default `0` | Optional external-training export control. When positive, the last N explicitly selected or manifest-split images form the validation partition; ordinary benchmark execution is unchanged. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.isac_ofdm_scenario`
**Name:** Synthetic ISAC OFDM allocation scenario
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `isac.ofdm_allocation_problem.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `example_count` | `integer` | no | default `64` | |
| `seed` | `integer` | no | default `0` | |
| `sensing_weight` | `number` | no | default `0.4` | |
| `snr_db` | `number` | no | default `10.0` | |
| `subcarriers` | `integer` | no | default `12` | |
| `total_power` | `number` | no | default `1.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "synthetic_frequency_selective_isac",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "synthetic_frequency_selective_isac",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "captured_isac_allocation_contract",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.kodak_files`
**Name:** Kodak image file manifest
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.files` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset_dir` | `string` | no | default `.noema/datasets/kodak` | |
| `limit` | `integer` | no | default `24` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.leo_ntn_tracking_scenario`
**Name:** Synthetic LEO-NTN Doppler and beam-handover scenario
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ntn.tracking_history.numpy` |
| `truth` | `ntn.future_state_truth.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `beam_count` | `integer` | no | default `9` | |
| `example_count` | `integer` | no | default `64` | |
| `history_length` | `integer` | no | default `6` | |
| `history_step_s` | `number` | no | default `0.5` | |
| `max_doppler_hz` | `number` | no | default `48000.0` | |
| `prediction_horizon_s` | `number` | no | default `1.0` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `15.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "bounded_kinematic_leo_pass",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "bounded_kinematic_leo_pass",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "captured_future_state_supervision",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.local_npz_images`
**Name:** Local NumPy image batch
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `array` | `string` | no | default `images` | |
| `dtype_policy` | `string` | no | default `strict_uint8`
values `strict_uint8`, `clip_round_uint8` | Reject non-uint8 input by default. clip_round_uint8 is an explicit lossy conversion that is recorded in provenance. |
| `path` | `string` | yes | | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.localization_geometry`
**Name:** Generated localization geometry
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `scene` | `ai_phy.localization_scene.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `anchor_count` | `integer` | no | default `4` | Number of anchors generated at equal intervals around the square perimeter. |
| `area_m` | `number` | no | default `20.0` | Side length of the generated two-dimensional localization area. |
| `example_count` | `integer` | no | default `16` | Number of synthetic target positions generated for this run. |
| `position_mode` | `string` | no | default `random_uniform`
values `random_uniform`, `fixed` | Generate seeded random targets or repeat one user-selected target position. |
| `seed` | `integer` | no | default `0` | Controls repeatable random target placement. |
| `target_x_m` | `number` | no | default `10.0` | Used only for fixed placement; must lie inside the configured area. |
| `target_y_m` | `number` | no | default `10.0` | Used only for fixed placement; must lie inside the configured area. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.localization_sensing_scenario`
**Name:** Localization/sensing geometry source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.localization_problem.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `area_m` | `number` | no | default `20.0` | |
| `example_count` | `integer` | no | default `16` | |
| `range_noise_m` | `number` | no | default `0.25` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `20.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "geometric_range_measurements",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "geometric_range_measurements",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_range_geometry",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_rt_scene_endpoint",
"notes": "Future Sionna RT scenes should materialize this same range/AoA target contract.",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `source.modulation_frames`
**Name:** Balanced modulation-frame source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `frames` | `ai_phy.modulation_frames.numpy` |
| `labels` | `ai_phy.modulation_labels.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `frame_count` | `integer` | no | default `96` | |
| `seed` | `integer` | no | default `23` | |
| `symbols_per_frame` | `integer` | no | default `128` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
The seeded symbol and label generator is benchmark data, not a learned component.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "numpy.ndarray"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.near_field_xl_mimo_scenario`
**Name:** Synthetic near-field XL-MIMO pilot scenario
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `near_field.array_observation.numpy` |
| `truth` | `near_field.range_angle_truth.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `angle_max_deg` | `number` | no | default `55.0` | |
| `angle_min_deg` | `number` | no | default `-55.0` | |
| `antennas` | `integer` | no | default `32` | |
| `carrier_frequency_ghz` | `number` | no | default `28.0` | |
| `example_count` | `integer` | no | default `64` | |
| `range_max_m` | `number` | no | default `5.0` | |
| `range_min_m` | `number` | no | default `0.5` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `15.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "spherical_wave_coherent_pilot",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "spherical_wave_coherent_pilot",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "captured_range_angle_supervision",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `source.random_bits`
**Name:** Synthetic random bit source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `batch_size` | `integer` | no | default `1` | |
| `bit_count` | `integer` | no | default `4096` | |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `seed` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
Synthetic discrete source bits are generated as benchmark artifacts and do not participate in gradient flow.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Given the same seed and bit count, random-bit source materializations must produce the same canonical uint8 bit vector.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.resource_allocation_scenario`
**Name:** OFDM subcarrier power-allocation scenario source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `problem` | `ai_phy.resource_allocation_problem.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `channel_tap_count` | `integer` | no | default `4` | |
| `seed` | `integer` | no | default `0` | |
| `snapshot_count` | `integer` | no | default `32` | |
| `snr_db` | `number` | no | default `10.0` | |
| `subcarrier_count` | `integer` | no | default `16` | |
| `total_power` | `number` | no | default `1.0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "synthetic_ofdm_frequency_response",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "synthetic_ofdm_frequency_response",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_ofdm_resource_state",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_sys_resource_state",
"runner": "differentiable_export",
"status": "planned"
}
]
```
### `source.retrieval_smoke`
**Name:** Image-text retrieval smoke source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `images` | `image.batch.numpy` |
| `targets` | `retrieval.targets.json` |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset` | `string` | no | default `retrieval_smoke`
values `retrieval_smoke` | |
| `image_size` | `integer` | no | default `224` | |
| `sample_ids` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.semantic_artifacts_smoke`
**Name:** Typed semantic artifact smoke source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `captions_candidate` | `text.caption.json` |
| `captions_reference` | `text.caption.json` |
| `clip_embeddings` | `vision.embedding.clip.numpy` |
| `detections_candidate` | `vision.detections.json` |
| `detections_reference` | `vision.detections.json` |
| `importance_map` | `semantic.importance_map.numpy` |
| `multimodal_embeddings` | `multimodal.embedding.numpy` |
| `rankings` | `retrieval.rankings.json` |
| `scene_graph` | `vision.scene_graph.json` |
| `segmentation_candidate` | `vision.segmentation_mask.numpy` |
| `segmentation_reference` | `vision.segmentation_mask.numpy` |
| `semantic_map` | `vision.semantic_map.json` |
| `video_frames` | `video.frame_sequence.numpy` |
| `vqa_candidate` | `vqa.answers.json` |
| `vqa_reference` | `vqa.answers.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset` | `string` | no | default `semantic_artifact_smoke`
values `semantic_artifact_smoke` | |
| `embedding_dim` | `integer` | no | default `8` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.task_labels_smoke`
**Name:** Task-oriented label/prediction smoke source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `candidate` | `task.predictions.json` |
| `reference` | `task.labels.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset` | `string` | no | default `task_smoke`
values `task_smoke` | |
| `sample_ids` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.text_dataset`
**Name:** Text semantic dataset batch
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `texts` | `text.batch.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset` | `string` | no | default `semantic_text_smoke`
values `semantic_text_smoke` | |
| `repeat_count` | `integer` | no | default `1` | |
| `sample_ids` | `string` | no | default `weather_report,robot_instruction,medical_note,traffic_alert,sensor_summary` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.vqa_manifest`
**Name:** Local VQA manifest source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `answers` | `vqa.answers.json` |
| `images` | `image.batch.numpy` |
| `questions` | `vqa.questions.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `image_root` | `string` | no | default `` | Optional root directory for relative image paths. |
| `image_size` | `integer` | no | default `224` | |
| `limit` | `integer` | no | default `8` | |
| `manifest_path` | `string` | yes | default `` | JSON/JSONL file with id, image, question, and answer fields. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.vqa_small_hf`
**Name:** VQA small sample source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `answers` | `vqa.answers.json` |
| `images` | `image.batch.numpy` |
| `questions` | `vqa.questions.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset_dir` | `string` | no | default `.noema/datasets/vqa_small` | |
| `download` | `boolean` | no | default `True` | |
| `image_size` | `integer` | no | default `224` | |
| `limit` | `integer` | no | default `8` | |
| `split` | `string` | no | default `validation`
values `test`, `train`, `validation` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `source.vqa_smoke`
**Name:** COCO/VQA-style smoke source
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `answers` | `vqa.answers.json` |
| `detections` | `vision.detections.json` |
| `images` | `image.batch.numpy` |
| `questions` | `vqa.questions.json` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `dataset` | `string` | no | default `coco_vqa_smoke`
values `coco_vqa_smoke` | |
| `sample_ids` | `string` | no | default `` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `python` |
| `dataset_capture` | `python` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "python",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "python",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
## wireless
### `wireless.carrier_phase_impairment`
**Name:** Residual carrier phase impairment
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `phase_truth` | `channel.carrier_phase_truth.numpy` |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `cfo_max_cycles_per_symbol` | `number` | no | default `0.01` | |
| `cfo_min_cycles_per_symbol` | `number` | no | default `-0.01` | |
| `initial_phase_max_rad` | `number` | no | default `3.141592653589793` | |
| `initial_phase_min_rad` | `number` | no | default `-3.141592653589793` | |
| `phase_noise_increment_std_rad` | `number` | no | default `0.04` | |
| `seed` | `integer` | no | default `0` | |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
The seeded carrier process is a benchmark impairment with explicit simulation truth.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "The same packet layout, parameters, and seed define the exact carrier trajectory.",
"type": "exact"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `wireless.channel`
**Name:** Wireless channel over complex symbols
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Optional inputs:**
| Name | Kind |
| --- | --- |
| `channel_state` | `channel.ofdm_channel_state.numpy` |
**Outputs:**
| Name | Kind |
| --- | --- |
| `rx_symbols` | `channel.rx_symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `carrier_frequency_ghz` | `number` | no | default `3.5` | |
| `channel` | `string` | no | default `awgn`
values `awgn`, `flat_rayleigh`, `interference_awgn`, `mimo_flat`, `ofdm_tdl`, `ofdm_cdl`, `urban_micro` | |
| `channel_state_mode` | `string` | no | default `none`
values `none`, `explicit` | explicit requires a bound channel_state artifact; none forbids one. |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `delay_spread_ns` | `number` | no | default `100.0` | |
| `fading_scope` | `string` | no | default `symbol`
values `symbol`, `source_item` | For flat Rayleigh fading, symbol draws an independent gain per complex symbol; source_item holds one gain constant over each source item. Source-item mode requires preserved item boundaries. |
| `interference_sir_db` | `number` | no | default `18.0` | |
| `interferers` | `integer` | no | default `0` | |
| `mobility_kmh` | `number` | no | default `3.0` | |
| `noise_mode` | `string` | no | default `snr_at_unit_power`
values `snr_at_unit_power`, `fixed_variance` | Choose whether physical-channel noise is derived from unit-power SNR or set directly as variance. |
| `noise_variance` | `number` | no | | Physical-channel noise variance used when noise mode is fixed variance. |
| `normalize_channel` | `boolean` | no | default `True` | |
| `num_ofdm_symbols` | `integer` | no | default `14` | |
| `ofdm_fft_size` | `integer` | no | default `64` | |
| `receiver_processing` | `string` | no | default `matched`
values `matched`, `none` | matched applies the preset's declared receiver (perfect-CSI equalization for fading presets); none returns the raw supported receive signal. |
| `rx_antennas` | `integer` | no | default `1` | |
| `seed` | `integer` | no | | Optional explicit operation seed. Omit it to derive the stream from the recipe master seed. |
| `snr_db` | `number` | no | default `12.0` | Unit-power reference SNR used to derive the physical-channel noise variance. |
| `subcarrier_spacing_khz` | `number` | no | default `15.0` | |
| `tdl_model` | `string` | no | default `A`
values `A`, `B`, `C`, `D`, `E` | |
| `tx_antennas` | `integer` | no | default `1` | |
| `wireless_backend` | `string` | no | default `auto`
values `auto`, `numpy`, `sionna` | Wireless implementation. auto resolves deterministically to the compatible NumPy materialization for ordinary sampled channels; select sionna explicitly to require Sionna. A bound explicit Sionna OFDM channel-state artifact necessarily selects Sionna. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Native Torch and Sionna 2.x/PyTorch AWGN and flat-Rayleigh materializations preserve gradients to transmitted symbols.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `sionna` |
| `dataset_capture` | `numpy`, `sionna` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "Only materializations with the same bound channel, receiver_processing, and channel_state_mode share a statistical-equivalence obligation; random samples need not be identical.",
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "numpy_awgn_matched",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_flat_rayleigh_matched",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_interference_awgn_matched",
"parameter_bindings": {
"channel": "interference_awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_mimo_flat_matched",
"parameter_bindings": {
"channel": "mimo_flat",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_ofdm_tdl_matched",
"parameter_bindings": {
"channel": "ofdm_tdl",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_ofdm_cdl_matched",
"parameter_bindings": {
"channel": "ofdm_cdl",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_urban_micro_matched",
"parameter_bindings": {
"channel": "urban_micro",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_awgn_unprocessed",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_interference_awgn_unprocessed",
"parameter_bindings": {
"channel": "interference_awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_flat_rayleigh_unprocessed",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_matched_artifact",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_matched_artifact",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_mimo_flat_matched_artifact",
"parameter_bindings": {
"channel": "mimo_flat",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_unprocessed_artifact",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_unprocessed_artifact",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_explicit_tdl_ofdm_matched_artifact",
"parameter_bindings": {
"channel": "ofdm_tdl",
"channel_state_mode": "explicit",
"receiver_processing": "matched"
},
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_awgn_matched",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_flat_rayleigh_matched",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_interference_awgn_matched",
"parameter_bindings": {
"channel": "interference_awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_mimo_flat_matched",
"parameter_bindings": {
"channel": "mimo_flat",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_ofdm_tdl_matched",
"parameter_bindings": {
"channel": "ofdm_tdl",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_ofdm_cdl_matched",
"parameter_bindings": {
"channel": "ofdm_cdl",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_urban_micro_matched",
"parameter_bindings": {
"channel": "urban_micro",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_awgn_unprocessed",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_interference_awgn_unprocessed",
"parameter_bindings": {
"channel": "interference_awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "numpy_flat_rayleigh_unprocessed",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_matched_artifact",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_matched_artifact",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_mimo_flat_matched_artifact",
"parameter_bindings": {
"channel": "mimo_flat",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_unprocessed_artifact",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_unprocessed_artifact",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_explicit_tdl_ofdm_matched_artifact",
"parameter_bindings": {
"channel": "ofdm_tdl",
"channel_state_mode": "explicit",
"receiver_processing": "matched"
},
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_awgn_matched_module",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched",
"wireless_backend": "auto"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_matched_pytorch_module",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_awgn_none_module",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none",
"wireless_backend": "auto"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_awgn_none_pytorch_module",
"parameter_bindings": {
"channel": "awgn",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_flat_rayleigh_matched_module",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched",
"wireless_backend": "auto"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_matched_pytorch_module",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "matched"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "torch_flat_rayleigh_none_module",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none",
"wireless_backend": "auto"
},
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_flat_rayleigh_none_pytorch_module",
"parameter_bindings": {
"channel": "flat_rayleigh",
"channel_state_mode": "none",
"receiver_processing": "none"
},
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `wireless.digital_link`
**Name:** Digital modulation over a simulated wireless link
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy`, `channel.coded_bits.numpy`, `channel.bits.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `bits` | `channel.payload_bits.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `carrier_frequency_ghz` | `number` | no | default `3.5` | |
| `channel` | `string` | no | default `awgn`
values `awgn`, `flat_rayleigh`, `interference_awgn`, `mimo_flat`, `ofdm_tdl`, `ofdm_cdl`, `urban_micro` | |
| `data_plane_backend` | `string` | no | default `auto`
values `auto`, `python_numpy`, `cpp_native` | Data-plane implementation for portable CPU kernels. auto resolves deterministically to Python / NumPy; select cpp_native explicitly to require the native extension. |
| `interference_sir_db` | `number` | no | default `18.0` | |
| `interferers` | `integer` | no | default `0` | |
| `mobility_kmh` | `number` | no | default `3.0` | |
| `modulation` | `string` | no | default `qpsk`
values `bpsk`, `qpsk`, `qam16` | |
| `noise_mode` | `string` | no | default `snr_at_unit_power`
values `snr_at_unit_power`, `fixed_variance` | Choose whether physical-channel noise is derived from unit-power SNR or set directly as variance. |
| `noise_variance` | `number` | no | | Physical-channel noise variance used when noise mode is fixed variance. |
| `num_ofdm_symbols` | `integer` | no | default `14` | |
| `ofdm_fft_size` | `integer` | no | default `64` | |
| `receiver_processing` | `string` | no | default `matched`
values `matched`, `none` | |
| `rx_antennas` | `integer` | no | default `1` | |
| `seed` | `integer` | no | | Optional explicit operation seed. Omit it to derive the stream from the recipe master seed. |
| `snr_db` | `number` | no | default `12.0` | Unit-power reference SNR used to derive the physical-channel noise variance. |
| `subcarrier_spacing_khz` | `number` | no | default `15.0` | |
| `tx_antennas` | `integer` | no | default `1` | |
| `wireless_backend` | `string` | no | default `auto`
values `auto`, `numpy`, `sionna` | Wireless implementation. auto resolves deterministically to the compatible NumPy materialization for ordinary sampled channels; select sionna explicitly to require Sionna. A bound explicit Sionna OFDM channel-state artifact necessarily selects Sionna. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `sionna` |
| `dataset_capture` | `numpy`, `sionna` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "operation-defined",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `wireless.miso_ofdm_csi`
**Name:** Correlated MISO-OFDM CSI realization
**Status:** `implemented`
**Inputs:**
None.
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `csi` | `channel.miso_ofdm_csi.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `carrier_frequency_ghz` | `number` | no | default `3.5` | |
| `channel_tap_count` | `integer` | no | default `6` | Tapped-delay count for the explicitly selected NumPy backend. |
| `delay_spread_ns` | `number` | no | default `100.0` | |
| `downlink_snr_db` | `number` | no | default `10.0` | |
| `mobility_kmh` | `number` | no | default `0.0` | |
| `normalize_channel` | `boolean` | no | default `True` | |
| `num_ofdm_symbols` | `integer` | no | default `1` | |
| `ofdm_fft_size` | `integer` | no | default `32` | |
| `sample_count` | `integer` | no | default `64` | |
| `seed` | `integer` | no | default `23` | |
| `subcarrier_spacing_khz` | `number` | no | default `30.0` | |
| `tdl_model` | `string` | no | default `A`
values `A`, `B`, `C`, `D`, `E` | |
| `tx_antennas` | `integer` | no | default `8` | |
| `tx_correlation_coefficient` | `number` | no | default `0.7` | |
| `wireless_backend` | `string` | no | default `sionna`
values `sionna`, `numpy` | |
**Differentiability (legacy `trainable_params`):** framework=`sionna`, gradient=`none`, trainable_params=`False`, exportable=`False`
CSI realizations are exogenous captured training data. Gradients begin at the feedback encoder input rather than flowing into random channel generation.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `sionna`, `numpy` |
| `dataset_capture` | `sionna`, `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "Backends share dimensions, average normalization, spatial correlation, delay profile, and seed semantics but do not claim identical realizations.",
"type": "statistical"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "float32 RI"
}
```
**Materializations:**
```json
[
{
"backend": "sionna",
"implementation": "sionna_tdl_correlated_miso_ofdm_csi",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_tdl_correlated_miso_ofdm_csi_capture",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "explicit_numpy_correlated_tapped_delay_fallback",
"notes": "Selected only when wireless_backend=numpy; Sionna requests never fall back silently.",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "explicit_numpy_correlated_tapped_delay_fallback",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `wireless.modulation_awgn_observation`
**Name:** Modulation frames with carrier uncertainty and AWGN
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `frames` | `ai_phy.modulation_frames.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `observation` | `ai_phy.modulation_iq_frames.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `carrier_phase_max_rad` | `number` | no | default `0.0` | |
| `carrier_phase_min_rad` | `number` | no | default `0.0` | |
| `frequency_offset_max_cycles_per_symbol` | `number` | no | default `0.0` | |
| `frequency_offset_min_cycles_per_symbol` | `number` | no | default `0.0` | |
| `seed` | `integer` | no | default `23001` | |
| `snr_db_max` | `number` | no | default `8.0` | |
| `snr_db_min` | `number` | no | default `8.0` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Complex AWGN can be reproduced by a differentiable tensor materialization.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `wireless.ofdm_channel_state`
**Name:** Sionna OFDM channel-state realization
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `symbols` | `channel.symbols.complex_numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `state` | `channel.ofdm_channel_state.numpy` |
| `symbols` | `channel.symbols.complex_numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `average_power_budget` | `number` | no | default `1.0` | Authoritative normalized average transmit-power budget per OFDM subcarrier for this channel-state scenario. |
| `capacity_multiplier` | `integer` | no | default `1` | Reserve additional OFDM channel-state capacity for allocation-aware variable-rate transport. |
| `carrier_frequency_ghz` | `number` | no | default `3.5` | |
| `delay_spread_ns` | `number` | no | default `100.0` | |
| `mobility_kmh` | `number` | no | default `3.0` | |
| `noise_variance` | `number` | no | default `0.1` | Noise reference attached to this CSI realization; the downstream physical channel must use the same variance. |
| `normalize_channel` | `boolean` | no | default `True` | |
| `num_ofdm_symbols` | `integer` | no | default `4` | |
| `ofdm_fft_size` | `integer` | no | default `16` | |
| `seed` | `integer` | no | | Optional explicit operation seed. Omit it to derive the stream from the recipe master seed. |
| `subcarrier_spacing_khz` | `number` | no | default `15.0` | |
| `tdl_model` | `string` | no | default `A`
values `A`, `B`, `C`, `D`, `E` | |
**Differentiability (legacy `trainable_params`):** framework=`sionna`, gradient=`none`, trainable_params=`False`, exportable=`False`
The artifact runner freezes a reproducible Sionna TDL realization so the allocator and channel consume identical CSI. No differentiable materialization is registered.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `sionna` |
| `dataset_capture` | `sionna` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "sionna",
"implementation": "sionna_3gpp_tdl_ofdm_frequency_response",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "sionna_3gpp_tdl_ofdm_frequency_response_capture",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `wireless.ofdm_delayed_csi`
**Name:** Delayed/noisy OFDM transmitter CSI
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `state` | `channel.ofdm_channel_state.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `actual_state` | `channel.ofdm_channel_state.numpy` |
| `transmitter_csi` | `channel.ofdm_channel_state.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `add_estimation_noise` | `boolean` | no | default `True` | Add independent complex Gaussian CSI-estimation error. |
| `allocation_ofdm_symbols` | `integer` | no | default `24` | Number of aligned old/current OFDM symbols retained per TDL block. |
| `capture_temporal_stride` | `integer` | no | default `1` | Dataset-capture downsampling stride along each TDL trajectory. Runtime allocation and scoring retain every aligned state. |
| `csi_estimation_snr_db` | `number` | no | default `20.0` | Mean old-channel power divided by complex CSI-estimation-error variance, in dB. |
| `csi_history_length` | `integer` | no | default `1` | Number of consecutive causal complex-CSI snapshots exposed to a history-aware transmitter policy. The newest snapshot is still feedback_delay_ofdm_symbols older than its paired current channel state. |
| `feedback_delay_ofdm_symbols` | `integer` | no | default `8` | CSI age in OFDM symbols. Old and current states are sliced causally from the same Sionna TDL trajectory. |
| `seed` | `integer` | no | | Optional explicit estimation-error seed. Channel aging itself comes from the upstream TDL trajectory. |
**Differentiability (legacy `trainable_params`):** framework=`numpy`, gradient=`none`, trainable_params=`False`, exportable=`False`
This operation freezes a causal pairing between old transmitter CSI and the later channel state applied by the physical link.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | None |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "none"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "causal_same_trajectory_csi_aging",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "causal_same_trajectory_csi_aging_capture",
"runner": "dataset_capture",
"status": "implemented"
}
]
```
### `wireless.pilot_observation`
**Name:** Noisy pilot observation
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `channel` | `ai_phy.channel_realization.numpy` |
| `pilots` | `ai_phy.pilot_pattern.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `ls_estimate` | `ai_phy.channel_estimation_ls_estimate.numpy` |
| `noise_variance` | `ai_phy.channel_noise_variance.numpy` |
| `observation` | `ai_phy.channel_estimation_observation.numpy` |
| `pilot_ls` | `ai_phy.channel_estimation_sparse_pilot_ls.numpy` |
| `pilot_mask` | `ai_phy.channel_estimation_pilot_mask.numpy` |
| `problem` | `ai_phy.channel_estimation_problem.numpy` |
| `truth` | `ai_phy.channel_estimation_truth.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `12.0` | |
| `wireless_backend` | `string` | no | default `auto`
values `auto`, `numpy`, `sionna` | Wireless implementation. auto resolves deterministically to NumPy; select sionna explicitly to require Sionna, with no runtime fallback. |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Pilot multiplication and additive noise have a direct differentiable materialization.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy`, `sionna` |
| `dataset_capture` | `numpy`, `sionna` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `wireless.range_observation`
**Name:** Noisy range observation
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `scene` | `ai_phy.localization_scene.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `observation` | `ai_phy.localization_observation.numpy` |
| `problem` | `ai_phy.localization_problem.numpy` |
| `truth` | `ai_phy.localization_truth.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `nlos_bias_m` | `number` | no | default `1.5` | |
| `nlos_probability` | `number` | no | default `0.0` | |
| `range_noise_floor_m` | `number` | no | default `0.05` | |
| `seed` | `integer` | no | default `0` | |
| `snr_db` | `number` | no | default `20.0` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
Euclidean range and additive measurement noise have a direct differentiable materialization.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```
### `wireless.ula_array_observation`
**Name:** ULA noisy array observation
**Status:** `implemented`
**Inputs:**
| Name | Kind |
| --- | --- |
| `scene` | `ai_phy.aoa_scene.numpy` |
**Optional inputs:**
None.
**Outputs:**
| Name | Kind |
| --- | --- |
| `observation` | `ai_phy.aoa_observation.numpy` |
| `problem` | `ai_phy.aoa_problem.numpy` |
| `truth` | `ai_phy.aoa_truth.numpy` |
**Parameters:**
| Name | Type | Required | Default / values | Description |
| --- | --- | --- | --- | --- |
| `antenna_count` | `integer` | no | default `8` | |
| `element_spacing_wavelengths` | `number` | no | default `0.5` | |
| `seed` | `integer` | no | default `0` | |
| `snapshot_count` | `integer` | no | default `64` | |
| `snr_db` | `number` | no | default `15.0` | |
**Differentiability (legacy `trainable_params`):** framework=`torch`, gradient=`full`, trainable_params=`False`, exportable=`True`
ULA steering, source mixing and additive noise have direct tensor materializations.
**Training capabilities:** built_in_fine_tuning=`False`, portable_replacement=`False`
**Portable trained-artifact ABI:** None.
**Backends:**
| Runner | Backends |
| --- | --- |
| `benchmark_run` | `numpy` |
| `dataset_capture` | `numpy` |
| `differentiable_export` | `torch`, `sionna` |
**Equivalence:**
```json
{
"reason": "No cross-backend equivalence claim declared.",
"type": "behavioral"
}
```
**Formats:**
```json
{
"artifact": "npz",
"tensor": "torch.Tensor"
}
```
**Materializations:**
```json
[
{
"backend": "numpy",
"implementation": "default",
"runner": "benchmark_run",
"status": "implemented"
},
{
"backend": "numpy",
"implementation": "default",
"runner": "dataset_capture",
"status": "implemented"
},
{
"backend": "torch",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
},
{
"backend": "sionna",
"implementation": "default",
"runner": "differentiable_export",
"status": "implemented"
}
]
```