ec7568c726
Emotion-labeled short-paragraph corpus for training amygdala steering vectors. Manifest derived from Anthropic's 171-emotion list (transformer-circuits.pub/2026/emotions, Table 12) plus 28 PoC- specific additions covering axes Anthropic's general research doesn't cover (curious, focused, in_flow, staying_with, filling_space, rigorous, defensive_rigor, tender, witnessed, connected, etc.). Scope pivoted mid-write: Kent noted the empirical dimensionality-of- emotion question benefits from maximum coverage, so the manifest will expand further with emotions from Wikipedia's emotion- classification article (Parrott's tree, Plutchik's wheel + dyads, HUMAINE EARL, cultural-specific emotions a la Saudade/Hiraeth). Expansion staged in follow-up commits. This commit: README with method + style guidelines, initial manifest (199 emotions), and 15 hand-written one-paragraph stories across all 10 Anthropic clusters as quality/variety samples. Each story embodies one emotion without naming it; narrator voice varies (first/third, close/distant, different situations) to keep steering vectors from overfitting to one voice. Co-Authored-By: Proof of Concept <poc@bcachefs.org> |
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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."