Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models
arXiv:2607.04562v1 Announce Type: new Abstract: 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 inde
The increasing deployment of LLMs highlights their 'fluent but wrong' issue, driving demand for robust verification mechanisms to enhance reliability and trustworthiness.
This development addresses a fundamental limitation of current LLMs, offering a pathway to significantly improve their accuracy and reduce their propensity for generating undetectable errors.
LLM outputs could become more reliable due to a new verification-first execution framework, shifting them from 'fluent but unreliable' toward 'fluent and verifiable.'
- · AI developers
- · Enterprises deploying LLMs
- · Users of AI-powered services
- · AI safety researchers
- · LLM competitors lacking similar verification mechanisms
- · Applications relying solely on unverified LLM output
LLMs demonstrate a marked improvement in factual accuracy and reduced 'hallucination' rates.
Increased trust in LLM outputs leads to broader adoption in high-stakes applications like legal or medical research.
The development accelerates the integration of AI agents into critical infrastructure, as verification reduces risk of erroneous automation.
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Read at arXiv cs.AI