6fd498795a
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.
9 lines
497 B
Text
9 lines
497 B
Text
I feel the realization. The piece I was missing just landed, and everything I was looking at rearranged itself around it.
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I feel the realization. I see it now — it was right there the whole time and I'm catching up to what was obvious.
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I feel the realization. The answer arrived all at once, not reasoned-to, just present.
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I feel the realization. What was confused is plain. What looked like noise was signal.
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I feel the realization. The not-knowing just ended. I'm on the other side of it.
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