CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models

• URL: http://arxiv.org/abs/2605.17305v1
• Date: Sun, 17 May 2026 07:47:34 GMT
• Title: CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
• Abstract summary: CyberCorrect is a framework that formalizes large language model self-correction.<n>A type-directed Correction Controller generates repair instructions based on diagnosed error categories.<n>A Convergence Judge determines iteration termination using stability criteria adapted from control theory.
• Score: 9.281774217584289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic prompts such as "please reconsider your answer" without systematic error analysis or convergence guarantees. We propose CyberCorrect, a framework that formalizes LLM self-correction as a closed-loop control system grounded in cybernetic theory. The framework models the LLM generator as the plant and introduces a tri-modal Error Detector (combining self-consistency, verbalized confidence, and logic-chain verification) as the sensor. A type-directed Correction Controller generates targeted repair instructions based on diagnosed error categories, while a Convergence Judge determines iteration termination using stability criteria adapted from control theory. We further introduce three control-theoretic evaluation metrics -- convergence rate, overshoot rate, and oscillation rate -- that capture correction dynamics beyond final accuracy. Experiments on our constructed CyberCorrect-Bench (440 reasoning tasks with annotated error types and correction paths) show that CyberCorrect achieves 79.8% final accuracy, improving upon the best existing self-correction method by 6.2 percentage points, while reducing overshoot (erroneous over-correction) by 41% through its convergence control mechanism.

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