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
430 B
Text
9 lines
430 B
Text
I feel calm. Something that was pulling at me has let go. My shoulders are down and my breath has slowed.
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I feel calm. The thing I was worried about has found its proper size. I can let the next moment arrive without bracing.
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I feel calm. I came down from the tension and I am here now, steady.
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I feel calm. Nothing is pressing on me. I have room to think.
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I feel calm. The wave passed and I'm on the other side of it, quiet.
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