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.
Previous direct/ had 'I feel X' first-person descriptions. The
training run showed they formed their own format-cluster: all 7
concepts leaned into the same 5-6 dims (d2455, d505, d2955,
d1236) with negative sign, while the 91 story-based concepts
leaned into those dims with positive sign. PCA found the
direct-vs-narrative format axis as a major variance direction,
isolating the 7 concepts in their own island.
Rewrite as 3rd-person narrative stories matching the rest of
the corpus. Keeps the explicit anchor phrases that worked ('it
all clicked into place', 'she was terrified', 'it was
anticipatory grief') but drops the first-person 'I feel X'
that was the format signal.
Each of the 7 concepts now has 3 narrative stories in varied
settings (conversations, drives, kitchens, mothers+grandmothers,
work, investigations). The blank-line-separated format is
still loaded by _load_direct_descriptions.
Also drop _baseline.txt — it was first-person ('I feel fine.
...') and would re-introduce the format mismatch. The ~90
story-based concepts provide plenty of narrative negatives
for each concept's training.
Chat-template retrain was a disaster (0.003 mean matched cosine vs
n20-v3; all 90+ concepts shifted). Root cause: the
steering-vectors library reads last-token activations, and with
chat template every sample ends in identical '<|im_end|>\n'
tokens — activations at that position encode 'end of assistant
turn', not content. PCA found template noise as its dominant axis.
Drop chat template; go back to raw text. Direct descriptions
('I feel X. ...') still have strong anchoring at their content
end without needing the template.
Also add per-concept spectrum logging (_pca_with_spectrum):
first_pc_ratio: λ₁ / Σλᵢ — concentration in top-1 PC
k_signal_at_90pct: how many PCs to reach 90% cumulative variance
effective_dim_signal: participation ratio over top-k (should ≈ k
if denoising is clean — Kent's spot check)
effective_dim_full: participation ratio over full spectrum
Signal/full ratio gives a sense of how much the long noise tail
is inflating the "dimensionality" measure.
Added direct/creative.txt — 'I feel creative. [...]' in 5
variants. Distinct from focused (narrow attention) and in_flow
(immersed). Creative = generative/expansive mode.
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.
"I feel the realization" is abstract, detached — reporting a
thought about a thought rather than inhabiting the moment.
"Aha!" is the actual sound of insight landing. Active, embodied,
present-tense.
Kent's insight: hand-written narrative stories bake scenario
phenomenology into the training text (on couch, in park, etc.)
and PCA picks up the scenario direction as the concept direction.
Strip out the scenario — just describe the *feeling*.
Format:
I feel X. [2-3 sentences of phenomenological texture]
The "I feel X" anchor kicks the model from analyzing → feeling.
The rest is the internal texture of the state. First person,
present tense, no narrative setup.
Text is wrapped in assistant-role chat template before being
tokenized — so we're training on the model-producing-this
hidden states, which is closer to the inhabited-state
representation we want for the readout.
Starting with the 6 concepts that had sign flips or wrong
clusters in the story-based training:
- terrified (was → cozy/resigned cluster)
- calm (was → grief_stricken cluster)
- onto_something (was → cozy/sensual cluster)
- resigned (was in warm-body-quiet cluster, shouldn't be)
- anticipatory_grief (was in warm-body-quiet cluster, shouldn't be)
- realization (new — the "aha" moment, distinct from onto_something)
5 descriptions each. New trainer: train_direct.py.
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).
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.
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.
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.
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>
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>
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>
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>
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>
