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 worker architecture, allowing execution to advance only when predefined correctness predicates are s...
| Authors | MY Pitsane, Hope Mogale |
|---|---|
| Primary category | cs.AI |
| Published | 6 July 2026 |
| Last updated at source | 1 September 2026 |
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