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 Heaviside Gate. HCRC combines model confidence with independent verification signals from a parallel wor
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- PossiblePossibly related (embedding) · 52%thu-pacman/chitu →
- PossiblePossibly related (embedding) · 46%chrisliu298/awesome-llm-unlearning →
- LinkedLinked via arxiv author · 85%MY Pitsane →
“Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models”
- LinkedLinked via arxiv author · 85%Hope Mogale →
“Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models”
- PossiblePossibly related (embedding) · 52%Evaluating J-space entropy as an error predictor across 7 datasets on Qwen3-4B [R] →