# Tensor Translation Layer artifact card

A Tensor Translation Layer converts model-independent canonical P into a frozen model's internal hidden-state space. It is intended for semantic or internal memory use rather than direct token forcing.

## Artifact

`pythia-1.4b-final-layer.ttl`

| Field | Value |
|---|---|
| Adapter class | TTL |
| Support level | semantic or internal |
| Format | `planner-cache-ttl-v1` |
| Base model | Pythia-1.4B |
| Hidden width | 2,048 |
| Attachment | GPT-NeoX layer 23 |
| Parameters | 2,707,464 |
| Canonical protocol | `pcm-canonical-p-v1` |
| Artifact SHA-256 | `72ef68d07ee27c37b90432d34d4be5c2c280ae1bcb08236e37a0e458c054d8d7` |

The TTL maps model hidden states to factorized canonical queries and selected canonical values back to model-hidden residuals. Its gate is conditioned on current hidden state, translated P, and canonical route features. The frozen Pythia base receives zero gradients.

The controlled 128-slot benchmark recorded 100% held-out state generation. In the matched causal test, changing only canonical P changed the answer. Wrong, historical, invalidated, router-disabled, and TTL-disabled conditions restored frozen candidate logits. This proves the tested Pythia path can consume internal state, not that every model can.

The artifact contains adapter tensors and compatibility metadata. It contains no base-model weights, P-cache state, conversation state, prompt tokens, KV, or optimizer state.

This evidence is specific to Pythia-1.4B. The tiny GPT-2 test is structural and does not establish trained semantic portability to another model family.