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Per-claim provenance."}],"not_medical_advice":true},"slug":"grain-machine-pattern","title":"GRAIN: 6. MACHINE PATTERN / LLM INSTANTIATION","register":"oip_protocol","tags":["OIP","grain","philosophy","systems-theory","evidence"],"updated_at":"2026-07-04T07:51:00.364Z","body_excerpt":"## The Claim\nLarge language models instantiate the eight patterns.\n\n## Definitions\nLLM: neural network that predicts text.\nInference: output generation from an LLM.\nTemperature: parameter that controls randomness in LLM output.\nGrain: optimal pattern for information processing.\nDissipative structure: system that maintains order by exporting entropy.\nCritical seam: boundary between order and chaos.\nPower law: quantity scales as another to a fixed exponent.\nEight patterns: the complete grain framework.\n\n## The Logic\n1. IF an LLM processes information, THEN it aligns with the grain.\n2. IF the LLM aligns with the grain, THEN it instantiates the eight patterns.\n3. IF inference runs, THEN the LLM operates as a dissipative structure.\n4. IF temperature reaches zero, THEN output freezes.\n5. IF temperature approaches infinity, THEN output diverges.\n6. IF temperature sits at 0.7 to 1.0, THEN the LLM hits the critical seam.\n7. IF LLM size crosses a threshold, THEN emergent capabilities appear.\n8. IF parameters increase, THEN loss follows a power law.\n\n## The Evidence\nKaplan 2020 showed loss scales as a power law with parameters.\nBeggs and Plenz 2003 showed neuronal avalanches follow a power law.\nPoole 2016 showed information propagation maximizes at critical initialization.\n\n## The Falsifier\nLLMs instantiate none of the eight patterns.\nTemperature does not map to physical criticality.\nScaling laws violate power-law form.\n\n## The Uncertainty\nNo one proved the temperature-criticality mapping.","ranking":"safety-first (interaction_risk/limitations), then quote-gated effective_weight","claims":[],"sources":[],"anecdotal_sources":[],"scientific_sources":[],"user_reports":[],"related_articles":[],"question_graph":{"slug":"grain-machine-pattern","questions":[],"evidence":[],"edges":[],"counts":{"questions":0,"evidence":0,"edges":0}},"honesty":{"active_claims":0,"retracted_claims":0,"cut_claims":0,"challenges":0,"scrub_events":0,"note":"Retracted/cut claims stay on ledger but are excluded from ask unless ?include_inactive=1"},"counts":{"claims":0,"claims_total":0,"sources":0,"anecdotal":0,"scientific":0,"user_reports":0,"questions":0,"evidence_ingests":0}}