New HCRC Framework Cuts LLM Errors to 0% Without Scaling Models

Large language models (LLMs) generate fluent but often incorrect outputs because autoregressive decoding lacks a mechanism to verify intermediate reasoning. The new HCRC framework from Pitsane and Mogale addresses this by reformulating inference as predicate-gated state transitions governed by a Heaviside Gate. It combines model confidence with independent verification signals from a parallel worker architecture, allowing execution to advance only when predefined correctness predicates are satisfied. This prevents invalid intermediate states from propagating, reducing epistemic entropy without modifying the underlying model.

Evaluated on software-engineering and reasoning tasks across thirteen proposers from four providers, HCRC reduced the false-completion rate (FCR) from 4-7% to 0% on capable proposers while remaining latency-competitive—sometimes even faster than the unwrapped model. On weaker proposers, it converted false completions into honest halts instead of corrupting downstream state. Beyond benchmarking, HCRC has operated for months as the production control plane of an agentic coding environment, authorizing file mutations, verification-driven progress reporting, and memory compaction. These results establish HCRC as a general framework for verification-driven LLM execution, showing that reliable reasoning can be achieved through principled execution control rather than model scale alone.