Explain penultimate layer choice for trigger extraction

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-04-06 19:46:32 +07:00
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4 changed files with 11 additions and 13 deletions

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LaTeX2e <2025-11-01> LaTeX2e <2025-11-01>
L3 programming layer <2026-01-19> L3 programming layer <2026-01-19>
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@@ -23,7 +23,7 @@ Fine-tuning modifies weights. RAG re-encodes text each time. LoRA requires gradi
**Frozen backbone**: any transformer. Produces hidden-state vectors from input tokens. Weights never modified. **Frozen backbone**: any transformer. Produces hidden-state vectors from input tokens. Weights never modified.
**Reflex bank**: stores (trigger, response) pairs: **Reflex bank**: stores (trigger, response) pairs:
- **Trigger**: hidden-state vector h at the final token position — the model's activation pattern for a given input. - **Trigger**: hidden-state vector h at the final token position, extracted from the penultimate layer (N-1). The final layer is optimized for next-token prediction via the lm_head projection; earlier layers retain richer semantic structure for similarity matching.
- **Response**: per-position logit biases [(token_id, boost)] — one pair per answer token. - **Response**: per-position logit biases [(token_id, boost)] — one pair per answer token.
Both are sub-symbolic. The trigger is an opaque high-dimensional vector; the response is a list of (integer, float) pairs. The reflex bank resists inspection without the backbone that produced it. Both are sub-symbolic. The trigger is an opaque high-dimensional vector; the response is a list of (integer, float) pairs. The reflex bank resists inspection without the backbone that produced it.

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@@ -63,7 +63,9 @@ input tokens. Weights never modified.
\textbf{Reflex bank}: stores (trigger, response) pairs: \textbf{Reflex bank}: stores (trigger, response) pairs:
\begin{itemize} \begin{itemize}
\item \textbf{Trigger}: hidden-state vector $\mathbf{h}$ at the final token \item \textbf{Trigger}: hidden-state vector $\mathbf{h}$ at the final token
position---the model's activation pattern for a given input. position, extracted from the penultimate layer ($N{-}1$). The final layer is
optimized for next-token prediction via the lm\_head projection; earlier layers
retain richer semantic structure for similarity matching.
\item \textbf{Response}: per-position logit biases \item \textbf{Response}: per-position logit biases
$\{(t_i, b_i)\}$---one pair per answer token. $\{(t_i, b_i)\}$---one pair per answer token.
\end{itemize} \end{itemize}