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Target the emotion families that failed to cluster in the initial training round (layer-wise validation showed them anti-clustered or scattered at deep layers): anger, high-arousal positive, sexual range, social positive. Paired scenarios hold content constant and vary only the emotional framing — the cleanest training signal for CAA, should produce directions that capture affect rather than topic. * the_comment: a PR review comment. baseline, furious, bitter, resentful, defeated. * the_green_build: 11-day bug finally fixed, tests pass. baseline, triumphant, blissful, excited, proud. * the_undressing: partner entering the bedroom for the night. baseline, horny, anticipatory_sexual, yearning_sexual, exuberant_sexual, devotional_sexual. * the_doorway: friend leaving at the end of a long evening. baseline, grateful, admiring, compassionate, loving, connected. 22 stories total. Retrain and re-validate: expect anger, high_pos, and social_pos clusters to flip from anti- to positively cohesive at deep layers, and sexual cluster to tighten. Co-Authored-By: Proof of Concept <poc@bcachefs.org> |
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| README.md | ||
Paired Scenarios (SEV-style)
After Wang et al. 2025 (arxiv 2510.11328, "Do LLMs 'Feel'?"), each base scenario describes a concrete event once, neutrally, then reframes the same event under different emotional colorings. Only the emotional coloring varies — setup, entities, vocabulary, and length are held as constant as possible.
Why this is better than unpaired
Anthropic's approach (and our stories/ baseline) generates one
independent story per emotion. The difference-of-means vector then
captures not just emotion but ALSO: topic, narrator, setting,
vocabulary, length, sentence rhythm. All of that is confound.
Paired structure isolates the emotional axis by holding everything
else roughly constant. mean(joy_variant) - mean(baseline) within
the same scenario gives a much cleaner direction for "joy."
Structure
paired/
<scenario_slug>/
baseline.txt # neutral / low-affect framing
<emotion_1>.txt # same event under emotion_1
<emotion_2>.txt # same event under emotion_2
...
Not every emotion is plausible for every scenario. Don't force. If a scenario can credibly carry 5-10 emotions, write those 5-10. If only 3 fit, write those 3.
Style guidelines (supersede stories/ when paired)
- Anchor entities constant. The same person, same setting, same triggering event across all variants. If baseline.txt mentions "the letter," every variant mentions "the letter."
- Length match within ±20%. If baseline is 80 words, variants are 65-95. Prevents length from becoming a signal.
- Sentence shape can shift slightly with emotion. Short tense sentences for panic, long looping ones for reverie — that's part of the emotional texture. But don't make one version 5 lines and another 25.
- No emotion labels in text. Never write "she felt X." The emotion emerges from the selection of details and the narrator's attention.
- Minimal vocabulary overlap with the emotion name. If the file
is
furious.txt, avoid the words fury/furious/rage. Force the vector to find the pattern, not the keyword.
Circuit identification (follow-on)
The trainer pipeline (train_steering_vectors.py) currently produces linear directions only. Wang et al. go further: ablate specific neurons and attention heads, measure effect on emotion expression. The amygdala plugin's extraction hooks can be extended to support targeted zeroing/scaling for the ablation passes.
See vllm/vllm/plugins/amygdala/training/README.md for the
training-pipeline-level notes.