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arXiv — cs.AI preprintsInternational7 October 2026

Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale

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arXiv:2610.07289v1 Announce Type: cross Abstract: Manual repair of program failures is time-consuming and disruptive for software developers, particularly during the pre-submit phase where test failures occur within continuous integration systems. While Automated Program Repair has seen significant advancement through Large Language Models, existing state-of-the-art techniques primarily focus on post-submit workflows, operating offline without the low-latency requirements necessary to assist developers in real-time within their flow before they switch context. In this paper, we introduce FlowA
— arXiv — cs.AI preprints

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