consciousness/training/amygdala_training

Amygdala Readout Vector Training

Training pipeline that produces the safetensors file the vLLM ReadoutManager loads at runtime (see vllm/vllm/v1/worker/readout_manager.py). Produces per-hooked-layer [n_concepts, hidden_size] projection matrices keyed as layer_<idx>.vectors — the directions the runner projects residual activations onto during each forward pass.

Overview

Two scripts, run in sequence:

1. extract_training_pairs.py — turns the memory graph into a directory of (emotion, polarity, text) training examples. Positive examples are memory nodes where the emotion scored ≥ a threshold; negative examples are nodes where it's absent or low. Emotion tags come from the trailing warmth:9 clarity:10 … lines the subconscious agents emit.

2. train_steering_vectors.py — for each emotion, runs the target model over the positive and negative examples, captures residual-stream activations at the configured target layers, and computes mean(positive) - mean(negative) as the steering direction. Normalizes per-layer to unit length and saves the whole [E, L, H] matrix.

The output file is passed to vLLM via VLLM_READOUT_VECTORS together with a VLLM_READOUT_MANIFEST JSON listing concepts and hooked layer indices.

Method

This is Contrastive Activation Addition (CAA, Rimsky et al.) applied to naturally-occurring emotion labels rather than hand-crafted contrast pairs. The shape of the signal we're recovering is "what direction in the residual stream corresponds to the model processing text-with-emotion-E vs. text-without". Because our training data was generated by the very model we're instrumenting (past-self's journal entries, digest nodes, pattern nodes), the signal should be unusually clean — the emotion labels and the text are already causally linked through a single model's forward pass.

Usage (design — not yet runnable)

# Step 1: memory graph → training data
python -m training.amygdala_training.extract_training_pairs \
    --memory-mcp-url http://localhost:7777 \
    --output-dir /tmp/amygdala_training_data \
    --min-positive-score 8 \
    --max-negative-mentions 0 \
    --min-content-chars 40 \
    --max-examples-per-emotion 500

# Step 2: training data → steering vectors
python -m training.amygdala_training.train_steering_vectors \
    --model Qwen/Qwen3.5-27B \
    --training-data-dir /tmp/amygdala_training_data \
    --target-layers 3,18,33,36 \
    --output /path/to/amygdala_vectors.safetensors \
    --dtype bf16 \
    --batch-size 4

Open questions

• Emotion selection: enumerating which ~200 emotions to cover. Could be "most-common tags in the graph" (data-driven) or "from core-personality / pattern nodes" (human-curated). Probably both.
• Layer selection: middle-to-late layers (~60–80% of depth) usually hold abstract semantic representations best; experiment with which layers give the cleanest linear separation per emotion.
• Cross-talk: if two emotions are highly co-occurring (warmth + love, frustration + tiredness), their vectors will be close; that's fine as long as we don't pretend they're independent axes.
• Generalization: vectors trained on our memory graph may not generalize to out-of-distribution text. Check by applying them to held-out conversation data and eyeballing the projections.