Refining bug reports with hierarchical evidence from an unverified agent trajectory plus repository review raises Pass@1 on SWE-Bench Lite from 41% to 59.67% (GPT-5-mini) and similarly for other agents.
RepoRepair: Leveraging code documentation for repository-level automated program repair
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RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
Neural Change Prediction generates mutation data to train bidirectional models linking code changes to behavioral effects for any executable program.
Reshaping outcome rewards, process signals, and rollout comparability in GRPO raises strict compile-and-semantic accuracy in agentic code repair from 0.385 to 0.535 under weak feedback.
citing papers explorer
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Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair
Refining bug reports with hierarchical evidence from an unverified agent trajectory plus repository review raises Pass@1 on SWE-Bench Lite from 41% to 59.67% (GPT-5-mini) and similarly for other agents.
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RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
RepoRescue creates a benchmark of 315 repositories and shows LLM agents rescue up to 41.5% with runtime enforcement and 62.7% when combining systems, with hardest cases requiring cross-file changes.
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Neural Change Prediction: Relating Software Changes to Their Effects and Vice Versa
Neural Change Prediction generates mutation data to train bidirectional models linking code changes to behavioral effects for any executable program.
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Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair
Reshaping outcome rewards, process signals, and rollout comparability in GRPO raises strict compile-and-semantic accuracy in agentic code repair from 0.385 to 0.535 under weak feedback.