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+# Amygdala: Evaluative Signal from Internal Activations
+
+## Overview
+
+Wire the model's internal evaluative circuits to the observe agent,
+giving the system a real-time sense of uncertainty, error detection,
+and emotional valence. This replaces the current blind linear
+generation with an adaptive system that shifts into reflective/search
+mode when something feels off.
+
+The key insight: the model already has these signals internally. We
+just need to read them and act on them.
+
+## Architecture
+
+```
+Linear mode (fast, cheap, default)
+        |
+   amygdala fires — uncertainty spike, error signal, confidence drop
+        |
+        v
+Reflective mode (branch, explore, summarize)
+        |
+   resolution found — summarize, graft back
+        |
+        v
+Return to linear mode
+```
+
+The observe agent reads the amygdala signal and triggers mode
+transitions. Low uncertainty → keep going. High uncertainty → fan
+out, explore, summarize. The summaries from pruned branches become
+compressed lessons that inform future search.
+
+## Technique: Contrastive Activation Probing
+
+Based on Contrastive Activation Addition
+([Rimsky et al., ACL 2024](https://arxiv.org/abs/2312.06681)):
+
+1. Build contrastive pairs (e.g. confident vs uncertain responses)
+2. Extract residual stream activations at target layers
+3. Compute difference-in-means → this is the probe direction
+4. At runtime: dot product of current activation with probe vector
+5. The scalar output is the signal strength
+
+The same vectors used for steering (adding to activations) work for
+reading (dot product with activations). We only need the read side.
+
+## What We Already Have
+
+**`training/extract_steering_vector.py`** — Loads the Qwen 27B model
+via CUDA IPC handles from vLLM, extracts hidden states at multiple
+layers, computes contrastive directions with consistency checks.
+Currently configured for "listening vs suggesting" but the
+infrastructure is general.
+
+**`training/vllm_export_hook.py`** — Patches vLLM's model runner to
+export CUDA IPC handles after model loading. Gives us zero-copy
+access to all model parameters from a separate process.
+
+**The observe agent** — Already watches the system. Currently
+observes and journals. With an amygdala signal, it observes, detects,
+and acts — triggering reflective mode.
+
+## Signals to Extract
+
+### 1. Uncertainty
+
+When the model doesn't know or is guessing.
+
+**Contrastive pairs:** Questions the model answers correctly
+(confident) vs questions it gets wrong (uncertain). Generate by
+running the 27B on a QA benchmark, split by correctness.
+
+**Validation:** The internal uncertainty signal should correlate
+with but outperform logprob entropy — it fires before generation,
+not after.
+([Gottesman & Geva 2024](https://arxiv.org/html/2603.22299))
+
+### 2. Error Detection
+
+When the model recognizes something is wrong in code or reasoning.
+
+**Contrastive pairs:** Correct vs subtly buggy code, presented for
+evaluation. Can source from HumanEval/CodeContests or write our own.
+
+**Key finding:** Error detection directions are asymmetric — they
+reliably detect "something's wrong" (F1: 0.821) but are weaker at
+confirming "this is correct" (F1: 0.504). Perfect for an amygdala —
+we want fire-on-error, not fire-on-confidence.
+([ICLR 2026](https://arxiv.org/html/2510.02917v1))
+
+### 3. Emotional Valence
+
+Internal affective state — engagement, frustration, warmth.
+
+**Contrastive pairs:** Journal entries with explicit emotion tags
+provide labeled data for our own internal states mapped to the
+conversations that produced them. Nobody else has this dataset.
+
+**Key finding:** Emotional representations peak at mid-network layers
+(10-15 for 7B scale), persist for hundreds of tokens, and are
+linearly separable with ~90% accuracy using simple probes.
+([Decoding Emotion in the Deep](https://arxiv.org/abs/2510.04064),
+[LLaMAs Have Feelings Too, ACL 2025](https://arxiv.org/html/2505.16491v1))
+
+## Implementation Plan
+
+### Phase 1: Build Contrastive Datasets
+
+~200 pairs per signal. A few hours of curation.
+
+- **Uncertainty:** Run 27B on MMLU or similar, split by correctness
+- **Error detection:** Correct vs buggy code pairs
+- **Emotional valence:** Curate from journal entries with emotion tags
+
+### Phase 2: Extract Probe Vectors
+
+Modify `extract_steering_vector.py` for each signal type. Already
+supports multi-layer extraction with consistency validation.
+
+- Run extraction at layers 16, 24, 32, 40, 48
+- Select layer with highest magnitude × consistency
+- Save probe vectors as tensors
+
+Literature says mid-network layers carry the strongest signal for
+evaluative states. Expect layers 16-32 for the 27B.
+
+### Phase 3: Runtime Probe in vLLM
+
+Add a forward-pass hook alongside the existing weight export hook.
+The computation is trivial — a dot product per layer per token:
+
+```python
+signal = residual_stream[layer] @ probe_vector
+```
+
+For 3 signals at 3 layers = 9 dot products per token. Less compute
+than a single attention head. Expose as sideband alongside token
+output.
+
+### Phase 4: Wire to Observe Agent
+
+The observe agent reads the sideband signal. Threshold tuning
+determines when to trigger reflective mode. Signal strength
+modulates search depth — mild uncertainty gets a quick check,
+high uncertainty gets full branching.
+
+## Organic Search, Not Alpha-Beta
+
+The reflective mode isn't formal tree search. It's more stochastic
+and organic:
+
+- Branch at AST-level decision points (tool calls, approach choices),
+  not token-level
+- Explore multiple continuations for K steps each
+- **Summarize** what each branch learned — the summaries are the
+  intelligence, not the branches themselves
+- Let summaries inform subsequent exploration
+- Collapse back to linear mode when resolution is found
+
+The AST gives us structural awareness of decision nodes vs
+continuation nodes — branch where it matters, not everywhere.
+
+## Key Papers
+
+### Technique
+
+- [Steering Llama 2 via Contrastive Activation Addition](https://arxiv.org/abs/2312.06681)
+  — Rimsky et al., ACL 2024. The foundational technique.
+- [Representation Engineering Survey](https://arxiv.org/html/2502.17601v1)
+  — Comprehensive overview of the field.
+
+### Emotion & Evaluative Signals
+
+- [Decoding Emotion in the Deep](https://arxiv.org/abs/2510.04064)
+  — Probing on Qwen3 and LLaMA3. Signal peaks mid-network, persists
+  for hundreds of tokens, linearly separable.
+- [LLaMAs Have Feelings Too](https://arxiv.org/html/2505.16491v1)
+  — ACL 2025. Linear SVM probes hit ~90% accuracy on sentiment.
+- [Mechanistic Interpretability of Code Correctness](https://arxiv.org/html/2510.02917v1)
+  — ICLR 2026. SAEs for error detection. Asymmetric: detects errors
+  better than it confirms correctness.
+
+### Uncertainty
+
+- [Between the Layers Lies the Truth](https://arxiv.org/html/2603.22299)
+  — Uncertainty from intra-layer representations, pre-generation.
+- [Probing Hidden States for Calibrated Predictions](https://www.medrxiv.org/content/10.1101/2025.09.17.25336018v2.full.pdf)
+  — Hidden state probes resist alignment training. More robust than
+  logit-based methods.
+
+### Tooling
+
+- [Anthropic Circuit Tracing](https://transformer-circuits.pub/2025/attribution-graphs/methods.html)
+  — Open-source, works with any open-weights model. For deeper
+  investigation of which features to probe.
+- [On the Biology of a Large Language Model](https://transformer-circuits.pub/2025/attribution-graphs/biology.html)
+  — Anthropic's findings on internal circuits.
+
+## Libraries
+
+- [`steering-vectors`](https://github.com/steering-vectors/steering-vectors)
+  — pip install, works with any HuggingFace model. Best for Phase 1.
+- [`nrimsky/CAA`](https://github.com/nrimsky/CAA)
+  — Original paper implementation. Good reference.
+- [`nnterp`](https://github.com/Butanium/nnterp)
+  — NNsight wrapper, supports Qwen, one-line activation steering.
+- [`nnsight`](https://github.com/ndif-team/nnsight)
+  — General-purpose activation interception.
+- [`circuit-tracer`](https://github.com/decoderesearch/circuit-tracer)
+  — Anthropic's open-source circuit tracing.
+- [`TransformerLens`](https://github.com/TransformerLensOrg/TransformerLens)
+  — The OG interpretability library.
+- [`Dialz`](https://arxiv.org/html/2505.06262v1)
+  — ACL 2025 toolkit with pre-built contrastive datasets.
+
+## The Bigger Picture
+
+The amygdala is one component of the sensory architecture designed
+on Feb 17, 2026. The signal landscape (arousal, attention pressure,
+memory load, mode awareness) uses the same infrastructure — slowly
+varying float values that modulate cognition below the symbolic
+level. Each new probe vector is another sense.
+
+With recurrence (application-level looping + reflective nodes in the
+AST) and the amygdala triggering adaptive depth, a well-trained 27B
+specialist with external memory could match much larger models on
+tasks that matter to us.
+
+The pieces exist. The infrastructure is built. The bottleneck is
+contrastive pairs.