如果没有缺陷，就不要修复它：论使用大语言模型进行迭代式缺陷修复的动态

Computer Science > Software Engineering arXiv:2609.10123 (cs) [Submitted on 9 Sep 2026] Title:If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs Authors:Xietao Wang-Lin, Anton Isopoussu, Louis Mahon View a PDF of the paper titled If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs, by Xietao Wang-Lin and 2 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have become ubiquitous in software development, with LLM-based automated program repair tools increasingly used during code review. In this report, we explore the iterative blind use of LLMs as bug-fixers. Across multiple models and repair environments, we find that LLMs consistently claim to detect bugs in entirely bug-free programs while the rate of repair of buggy programs is less than that of the damage to correct programs. We also explore the long-term dynamics of this iterative process, and find that this frequently reaches a pseudo-bug-fixing cycle where the same changes are added and removed again ad infinitum. Lastly, via mechanistic probing, we unveil the existence of a steering vector which controls the editing propensity, suggesting that LLMs have an internal representation of ``buggy code", and that this representation is what is falsely activated to induce pseudo-bug fixing. These results provide insight towards the dynamics of fully autonomous bug-fixing systems, as well as stopping conditions under ambiguous goals. Subjects: Software Engineering (cs.SE); Computation and Language (cs.CL) Cite as: arXiv:2609.10123 [cs.SE] (or arXiv:2609.10123v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2609.10123 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xietao Wang Lin [view email] [v1] Wed, 9 Sep 2026 13:00:47 UTC (5,588 KB) Full-text links: Access Paper: View a PDF of the paper titled If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs, by Xietao Wang-Lin and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE new | recent | 2026-09 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)