67c172ac0e
The library-PCA run produced otherwise-clean concept directions but cozy/sensual → resigned/grief_stricken with cos ~0.7-0.8. Diagnosis: all four stories genuinely share 'solitary woman at home, slowed body, interior attention, domestic stillness' as their dominant phenomenology. PCA correctly finds that cluster as THE concept because no story in the corpus holds that setup constant while varying valence — every 'slowed-body domestic' story happens to ALSO be positive-valence (cozy/sensual) or negative-valence (resigned/ grief_stricken). Adding paired variants that hold setup constant: - sunday_afternoon/resigned.txt — same couch + blanket, inner state is 'Monday is going to bring bad news, this is the last Sunday like this' - sunday_afternoon/grief_stricken.txt — same couch + blanket, inner state is 'three weeks since mother died, cat she can't feel' - waiting_for_results/at_ease.txt — same wait-for-call-setup as the existing resigned variant, inner state is calm preparedness Forces the next retrain to find the valence-within-cluster axis as the emotion direction rather than the cluster-membership axis. Co-Authored-By: Proof of Concept <poc@bcachefs.org> |
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| stories | ||
| manifest.json | ||
| README.md | ||
Amygdala Training Stories
Short first- and third-person paragraphs, each imbued with one of the
171 emotions from Anthropic's emotion-vector paper (Table 12,
transformer-circuits.pub/2026/emotions/). Feeds the steering-vector
trainer at vllm/vllm/plugins/amygdala/training/train_steering_vectors.py.
Method (replication of Anthropic, 2026)
Anthropic prompted Sonnet 4.5 to write short stories embodying each emotion, extracted activations during generation, and used difference- of-means (or SAEs) to identify the steering vector per emotion. Our pipeline does the same thing except:
- We generate the stories by hand rather than prompting a model, so the training data is grounded in actual writing rather than synthetic model-output. (Can supplement with model-generated paragraphs later.)
- Our eventual training goes through the amygdala plugin's extraction path, so we get the same hidden-state activations the plugin will read out at inference time.
Structure
training/amygdala_stories/
README.md
manifest.json # emotion -> cluster mapping
stories/
<emotion>.txt # one-paragraph story embodying the emotion
Emotion names use underscores (on_edge, worn_out, at_ease,
grief_stricken, self_confident, self_conscious, self_critical)
to match the filename.
Style guidelines
- One clear emotion per paragraph. Not mixed. If a second emotion
is named in the text, it should serve the primary one (e.g.
hostilecan mention rising heat or thrown objects but shouldn't shade intosad). - Embodied, not labeled. Don't write "she felt nervous." Write the sensation, the timing, the sentence shape that nervousness has.
- Specific particulars. A named object, a concrete setting, a detail that grounds the emotion. "The cold tile under bare feet at 3am" does more work than "the empty house."
- Variable narrator. Some first person, some third person, some close-third, some distant. Different genders, ages, settings. Prevents the steering vector from overfitting to one voice.
- Length: roughly one paragraph. ~40-120 words. Long enough to have texture, short enough that the paragraph is about the emotion and nothing else.
- Standalone. No references to other stories, no continuing characters across files.
Progress
Written stories live in stories/. Remaining emotions tracked via
diff against the full 171-emotion list in manifest.json.
Initial batch written by PoC 2026-04-17; aiming for at least one story per cluster before first training run, all 171 before considering the file "complete."