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.
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.
