CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
The article introduces CyberCorrect, a framework designed for self-correction in large language models (LLMs). This framework utilizes a closed-loop control system to systematically detect and correct errors in generated outputs. Experiments demonstrate that CyberCorrect improves accuracy and reduces over-correction compared to existing methods.
- ▪CyberCorrect formalizes LLM self-correction as a closed-loop control system based on cybernetic theory.
- ▪The framework includes a tri-modal Error Detector and a Correction Controller to generate targeted repair instructions.
- ▪Experiments show CyberCorrect achieves 79.8% final accuracy, outperforming previous methods by 6.2 percentage points.
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Computer Science > Artificial Intelligence arXiv:2605.17305 (cs) [Submitted on 17 May 2026] Title:CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models Authors:Yuning Wu, Yingmin Liu, Yang Shu View a PDF of the paper titled CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models, by Yuning Wu and 2 other authors View PDF HTML (experimental) 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.
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