用控制论框架让大模型自动发现并修正错误，效果更稳更准。
CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
- 将大模型自修正视为闭环控制系统，分三步诊断、纠错、判断终止。
- 在440个推理任务上达到79.8%准确率，比现有方法高6.2个百分点。
- 首次引入收敛率、过冲率等动态指标，适合研究模型修正机制的人。
大语言模型的自修正能力仍依赖于随意提示，缺乏系统性分析与收敛保证。我们提出CyberCorrect，一个基于控制论的闭环自修正框架。该框架将生成器视为被控对象，设计三模态误差检测器（结合自一致性、置信度表述和逻辑链验证）作为传感器，类型指导的纠错控制器根据错误类别生成精准修复指令，收敛判别器则依据控制理论中的稳定性准则决定迭代终止。我们进一步提出三种控制论评估指标——收敛率、过冲率和振荡率，以捕捉修正过程的动态特性。在自建的CyberCorrect-Bench（含440个带错误类型与修正路径标注的推理任务）上的实验表明，CyberCorrect最终准确率达79.8%，优于最优现有方法6.2个百分点，同时通过收敛控制将过冲率降低41%。
原文摘要 · Abstract (English)
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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