708c72b26e
Previous rewrite used 'she was terrified', 'it was anticipatory grief', 'he was resigned' as explicit emotion anchors. Training showed 6 of the 7 concepts still cluster together at cosines 0.52-0.71 — because the 'she was [emotion]' pattern is a shared stylistic feature distinct from the rest of the corpus, which conveys emotion implicitly through phenomenology. Rewrite without the anchor. State conveyed through action and body: 'her body locked down', 'his mind had stopped reaching', 'the loss hadn't come yet but she was already inside it'. Matches the corpus style of existing stories like sunday_afternoon/content which says 'nothing she wanted right now, nothing missing' not 'she was content'. Accept some loss of PCA signal strength in exchange for the concepts living in their semantically correct neighborhoods rather than forming a stylistic island. |
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| .. | ||
| direct | ||
| paired | ||
| 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."