Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models

Large language models (LLMs) generate fluent outputs that can be wrong. Unlike humans, who often exhibit cues when providing false information, LLMs produce errors that are difficult to detect because autoregressive decoding provides no mechanism for verifying intermediate reasoning before state progression. We introduce Heaviside Continuity of Rolling Coefficients (HCRC), a verification-first execution framework that reformulates inference as predicate-gated state transitions governed by a Heav

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ethics.ai (6 July 2026), “Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models,” evidence record 217, https://ethics.ai/record/217 (originally published by arXiv).

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