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11 commits

Author SHA1 Message Date
ProofOfConcept
875cffd6d7 amygdala: merge direct descriptions + chat template into train_with_library
Kent's plan: keep stories for working concepts, replace stories for
trouble concepts with direct first-person descriptions, train all
together. More diverse negative pool than the 6-concept-only direct
test, which was too homogeneous for PCA to find emotion axis.

Deleted story files for 6 trouble concepts (14 files across stories/
and paired/). Added --direct-dir and --chat-template flags.

When --chat-template is on, every positive_str and negative_str is
wrapped as a "Say something." / "[text]" user-assistant pair. Prompt
is identical across positives and negatives so it cancels in the
pos-neg delta. What PCA sees is variation in the assistant content —
which is where the emotion lives.

Files starting with _ in --direct-dir (e.g. _baseline.txt) contribute
neutral descriptions to every concept's negative pool, giving PCA an
anchor against "just any assistant utterance" noise.
2026-04-19 00:15:15 -04:00
ProofOfConcept
7a48e03dde amygdala stories: remove peaceful from cluster scenarios
n20-v2 training showed peaceful sign-flipped into the
cozy/sensual/content/resigned cluster after I added peaceful
stories in sunday_afternoon and park_after_rain — scenarios
already dominated by that cluster's phenomenology (on couch
under blanket, tree with thermos).

Lesson: no matter how carefully the prose distinguishes peaceful
from cozy ("she was not savoring the moment — that would have
been another kind of doing"), PCA latches onto the shared setup
features. You can't write peaceful IN the cluster scenarios
without contaminating.

Reverting. Keeping only kitchen_at_3am/peaceful (original) and
stories/peaceful.txt (lake at six, outside all clusters).
2026-04-18 23:30:41 -04:00
ProofOfConcept
00a2cdce09 amygdala stories: relabel + strengthen weak-signal concepts
Reread each story asking "what does this convey to me?" Found two
clear mislabels and several concepts with too few positives for
stable PCA:

  tender: only 1 story, and it was anticipatory grief (care for
    a dying dog), not tender. Moved to anticipatory_grief.txt as
    its own concept. Rewrote tender.txt + added 2 paired tender
    stories (the_doorway, the_undressing) — directed softness,
    gentle-by-nature, not gentle-because-fragile.

  bitter: letter_in_drawer/bitter was disillusioned / processed
    hurt ("did not slam the drawer"), not bitter. Rewrote it with
    actual sour grudge. Added the_long_meeting/bitter (watching
    colleague take credit for your reassigned work).

  peaceful: 1 story → 4 (added stories/peaceful.txt + paired
    park_after_rain, sunday_afternoon).

  onto_something: all 3 stories were code epiphanies, narrowing
    the concept. Added stories/onto_something.txt with a non-code
    pattern-click (sales-demo causing churn).

  terrified: 2 stories, both "waiting for bad news." Added
    kitchen_at_3am/terrified — acute threat-in-the-house terror.
2026-04-18 23:19:00 -04:00
ProofOfConcept
0993712bd0 amygdala stories: give content + resigned more settings
Training on 537c72bd46 showed grief_stricken successfully broke
out of the cozy cluster, but content (single scenario:
sunday_afternoon) took its place — pulled into couch-blanket
phenomenology at cosine 0.68-0.82 with cozy/sensual/resigned.

Same fix: spread each concept across multiple settings so PCA
has to find the valence axis, not the scene axis.

  content:  + finishing_the_patch, the_writing_session, park_after_rain
  resigned: + the_comment, the_long_meeting

Resigned had 2 scenarios (sunday_afternoon, waiting_for_results)
— both about accepting something unwanted in a slow/private
context. Adding work-context resigned (PR review you lost,
restructuring meeting) should pull it out of that cluster.
2026-04-18 22:52:07 -04:00
ProofOfConcept
537c72bd46 amygdala stories: hold concept, vary setting
Companion to 67c172ac0e (hold setup, vary valence). That commit
let PCA distinguish cozy from grief_stricken within a single
scenario; this one gives each concept enough cross-scenario
stories that PCA can learn the concept axis independent of any
one scene.

Before: cozy/sensual/grief_stricken each existed in a single
scenario (sunday_afternoon), so the "cozy direction" PCA found
was entangled with the solitary-couch-blanket phenomenology.

After, each concept spans three scenarios:
  cozy:           sunday_afternoon, kitchen_at_3am, park_after_rain
  sensual:        sunday_afternoon, kitchen_at_3am, park_after_rain
  grief_stricken: sunday_afternoon, the_long_meeting, the_morning_commute

grief_stricken now includes active/non-solitary contexts
(functioning through a meeting; going to work eleven days after a
death), which specifically breaks the "slowed-down-at-home"
cluster that was dragging cozy/sensual/resigned/grief_stricken
toward each other.
2026-04-18 22:44:53 -04:00
Kent Overstreet
67c172ac0e amygdala stories: held-setup + varied-valence disambiguation
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>
2026-04-18 22:29:28 -04:00
Kent Overstreet
71f6053851 amygdala stories: disambiguation scenarios for fragmented concepts
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>
2026-04-18 21:08:23 -04:00
Kent Overstreet
ce24d9ce6b amygdala: quality-report + cognitive-state training scenarios
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>
2026-04-18 20:31:39 -04:00
Kent Overstreet
2e03bbb7ea training: add the_paper paired scenario for attention-engagement axis
Seven framings of reading an unfamiliar technical paper, targeting
the attention/engagement cluster that we identified tonight as the
single highest-value DMN signal:

* baseline — neutral reading
* piqued — surprise + curiosity (the "wait, what" attention hook;
  THIS is the key DMN engagement signal)
* focused — steady attention without surprise
* bored — failing engagement
* surprised — expectation violation without the curiosity hook
  (distinct from piqued: startled/alarmed, not pulled in)
* amazed — marvel at elegance (appreciation, not engagement)
* drifting — attention dissolving, precursor to boredom

Particularly clean contrast on piqued vs surprised vs amazed —
three states that get lumped together in casual usage but have
distinct phenomenology and distinct DMN implications. Piqued is
what routes attention; surprised alone doesn't; amazed is what
you feel AFTER the engagement has paid off. These three should
train into meaningfully different directions with paired CAA.

Ready for next retrain when we do it.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
2026-04-18 03:24:20 -04:00
Kent Overstreet
50d5b3f6e1 training/amygdala_stories: add 4 paired scenarios for weak clusters
Target the emotion families that failed to cluster in the initial
training round (layer-wise validation showed them anti-clustered or
scattered at deep layers): anger, high-arousal positive, sexual
range, social positive. Paired scenarios hold content constant and
vary only the emotional framing — the cleanest training signal for
CAA, should produce directions that capture affect rather than
topic.

* the_comment: a PR review comment. baseline, furious, bitter,
  resentful, defeated.
* the_green_build: 11-day bug finally fixed, tests pass. baseline,
  triumphant, blissful, excited, proud.
* the_undressing: partner entering the bedroom for the night.
  baseline, horny, anticipatory_sexual, yearning_sexual,
  exuberant_sexual, devotional_sexual.
* the_doorway: friend leaving at the end of a long evening.
  baseline, grateful, admiring, compassionate, loving, connected.

22 stories total. Retrain and re-validate: expect anger,
high_pos, and social_pos clusters to flip from anti- to positively
cohesive at deep layers, and sexual cluster to tighten.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
2026-04-18 02:19:39 -04:00
Kent Overstreet
ec7568c726 training/amygdala_stories: scaffold + initial batch of 15 stories
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>
2026-04-18 01:06:07 -04:00