71f6053851
Three new paired scenarios targeting the concepts that came out fragmented or collapsed in the L58-63 quality analysis: - sunday_afternoon/ — same setup (couch, blanket, Sunday light), three phenomenological framings for content/cozy/sensual. The previous stories for these three differed in setting as well as phenomenology, which let "comfortable body at home" dominate the shared signal. Locking the setting forces the model to isolate what each concept adds: life-rightness (content) vs. warm-shelter (cozy) vs. sensory-aliveness (sensual). - the_writing_session/ — essay drafting under deadline. in_flow / anxious / stuck variants force the cognitive-state family apart on the same cognitive task. in_flow specifically targets the transparent-effort phenomenology (hands-followed, time dilation) rather than the broader feel-good it was absorbing. - the_morning_commute/ — anchors anxious to performance/work-anxiety flavor, paired with calm. The 5 existing anxious stories were phenomenologically diverse (performance, social, existential); this adds a specific homogeneous instance to pull the centroid. After retraining: expect first_pc_variance_ratio to rise for in_flow and anxious, and nearest_concepts cosine to drop for content/cozy/sensual. Co-Authored-By: Proof of Concept <poc@bcachefs.org> |
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| 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."