consciousness/training/amygdala_stories/direct/onto_something.txt

He'd been working through the symptoms for an hour, steady and methodically making progress, eliminating one possibility after another — he was onto something. The answer wasn't in view yet, but it was close. He kept asking the next question.

She'd been going through the witness statements, steady and methodically, looking for the inconsistency — she was onto something. The four of them all described the same drive in slightly different orders. One of them had gotten the sequence wrong. She didn't know yet which one, but she was going to.

He'd been piecing together his brother's behavior over months — the missed calls, the abrupt move, the strange money — steady and methodically. He was onto something. The picture wasn't complete, but the shape of it was forming. He kept following the thread.
amygdala: rewrite direct/ as narrative stories matching corpus format 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. 2026-04-19 00:59:31 -04:00			`He'd been working through the symptoms for an hour, steady and methodically making progress, eliminating one possibility after another — he was onto something. The answer wasn't in view yet, but it was close. He kept asking the next question.`
amygdala: direct phenomenological description approach 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. 2026-04-19 00:04:28 -04:00
amygdala: rewrite direct/ as narrative stories matching corpus format 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. 2026-04-19 00:59:31 -04:00			`She'd been going through the witness statements, steady and methodically, looking for the inconsistency — she was onto something. The four of them all described the same drive in slightly different orders. One of them had gotten the sequence wrong. She didn't know yet which one, but she was going to.`
amygdala: direct phenomenological description approach 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. 2026-04-19 00:04:28 -04:00
amygdala: rewrite direct/ as narrative stories matching corpus format 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. 2026-04-19 00:59:31 -04:00			`He'd been piecing together his brother's behavior over months — the missed calls, the abrupt move, the strange money — steady and methodically. He was onto something. The picture wasn't complete, but the shape of it was forming. He kept following the thread.`