ce24d9ce6b
Training pipeline additions:
- `--quality-report` flag: after producing per-concept vectors, compute
per-concept diagnostics and write quality.json. Metrics per concept:
* SVD of centered positives -> first_pc_variance_ratio (rank
analysis; >0.7 clean, <0.4 fragmented)
* Per-story alignment cosines (stories agree or disagree)
* Single-neuron alignment: best cosine(direction, W_down column)
at each target layer (>0.6 = essentially one MLP neuron)
* Top-2 outlier stories by alignment (candidates for
mislabeling or off-topic)
* Top-5 nearest concepts by cosine (cross-concept contamination)
Triage summary printed at end.
New paired scenarios for cognitive-process states (for alpha-beta
pruning): tracing_a_bug, reading_unfamiliar_code, finding_the_abstraction.
Each has baseline + onto_something / stuck / in_flow / determined
variants.
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."