arXiv:2611.14960v1 Announce Type: new Abstract: Coding agents can solve a task well before they stop working on it. We characterize \emph{termination lag}--the number of steps between an agent's first correct patch and the end of its run--across five models, two agent frameworks, and three SWE-bench datasets by recording and testing every distinct repository patch generated during execution. Our analysis reveals that 38--82% of successful runs achieve a correct solution on their initial source-code edit. However, agents continue to expend substantial effort after correct patches, with post-solution steps accounting for a median of 35--37% for Qwen3-Coder and 54--60% for Laguna-XS.2 in successful runs on SWE-bench Verified and Multilingual. Agents spend most of these additional steps verifying their work and exploring the repository. In a small fraction of cases (1--8%), this continued activity leads agents to undo their earlier correct patch, leaving the final solution incorrect.
Termination Lag: Coding Agents Solve Early but Stop Late
A study of five models, two agent frameworks and three SWE-bench datasets found that 38–82% of successful coding-agent runs reach a correct solution on their first source-code edit, yet agents keep working well past that point, according to arXiv paper 2611.14960v1. Post-solution steps account for a median of 35–37% of successful runs for Qwen3-Coder and 54–60% for Laguna-XS.2 on SWE-bench Verified and Multilingual, with most of that effort spent verifying work and exploring the repository; in 1–8% of cases the continued activity undoes the earlier correct patch and leaves the final solution incorrect.
Run your AI side-project on zahid.host
EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.