> ## Documentation Index
> Fetch the complete documentation index at: https://sdk.observability.getonex.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Signal Reference

PyTorch signals exported by `OneXMonitor.watch(model)` (non-exhaustive). Use `assessments=[...]` to filter fields; when unset, full payloads are sent.

## Per-forward signals

| `signal_type`             | When                                  | Purpose                                                                        |
| ------------------------- | ------------------------------------- | ------------------------------------------------------------------------------ |
| `representation.<policy>` | After each `forward` (causal / GPT-2) | OOD-style **`embedding_vector`** (`last_token`, `prompt_last`, or `mean_pool`) |
| `output_distribution`     | After each `forward`                  | Entropy, top-k confidence, logit stats, optional **`logits_extras`**           |
| `input_statistics`        | After each `forward`                  | Input shape / norm / token stats                                               |
| `llm.final_norm`          | Final norm layer (causal LMs)         | Pre-`lm_head` hidden state summaries                                           |
| `llm.lm_head`             | `lm_head` forward                     | Bounded logits summary                                                         |

## Per-layer signals (architecture-dependent)

| Pattern                                | Description                                    |
| -------------------------------------- | ---------------------------------------------- |
| `hidden_states.causal_decoder.layer_*` | Decoder block output (Qwen, Llama, etc.)       |
| `hidden_states.bert_encoder.layer_*`   | BERT encoder layers                            |
| `hidden_states.gpt_block.layer_*`      | GPT-2 blocks                                   |
| `attention.layer_*`                    | Attention weights (when returned by the model) |
| `embeddings.*`                         | Embedding layer outputs                        |
| `generic_activation.*`                 | Linear / LayerNorm statistics                  |
| `pre_classification.*`                 | Classifier / head inputs (BERT-style OOD)      |

## `llm_extension` (when enabled)

Optional nested JSON on exported signals with generation context, output summaries, and model metadata. See [Configuration Reference](/configuration) for related flags.

## Qwen / PyTorch tips

* Call **`monitor.watch()`** on **`AutoModelForCausalLM`** (not only `model.model`) so logits and representation use the full forward output.
* For long generations, raise **`signal_queue_maxsize`** if the exporter drops signals.