CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
Yuning Wu, Yingmin Liu, Yang Shu

TL;DR
CyberCorrect introduces a cybernetic, closed-loop framework for systematic self-correction in large language models, improving accuracy and stability through error detection, targeted correction, and convergence control.
Contribution
It formalizes LLM self-correction as a control system with novel error detection, correction, and convergence metrics, advancing beyond ad hoc prompt-based methods.
Findings
Achieves 79.8% final accuracy on 440 reasoning tasks.
Improves correction accuracy by 6.2 percentage points over existing methods.
Reduces overshoot rate by 41% through convergence control.
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,…
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