2e03bbb7ea
Seven framings of reading an unfamiliar technical paper, targeting the attention/engagement cluster that we identified tonight as the single highest-value DMN signal: * baseline — neutral reading * piqued — surprise + curiosity (the "wait, what" attention hook; THIS is the key DMN engagement signal) * focused — steady attention without surprise * bored — failing engagement * surprised — expectation violation without the curiosity hook (distinct from piqued: startled/alarmed, not pulled in) * amazed — marvel at elegance (appreciation, not engagement) * drifting — attention dissolving, precursor to boredom Particularly clean contrast on piqued vs surprised vs amazed — three states that get lumped together in casual usage but have distinct phenomenology and distinct DMN implications. Piqued is what routes attention; surprised alone doesn't; amazed is what you feel AFTER the engagement has paid off. These three should train into meaningfully different directions with paired CAA. Ready for next retrain when we do it. 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.