8 papers
Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework
Chenglin Li, Yisen Xu, Zehao Wang +3
Autonomous coding agents powered by large language models can now generate code patches directly from bug reports, but a fundamental gap remains: once a patch is produced, no mecha…
Turning Interaction History into Execution State: A Runtime Layer for Long-Horizon Coding Agents
Zehao Wang, Yisen Xu, Chenglin Li +5
Long-horizon coding agents accumulate hundreds of actions and observations in their trajectories, yet nothing in this record indicates which observations still describe the reposit…
Reusing Past Repairs Through Hierarchical Trajectory Abstraction for Coding Agents
Yisen Xu, Jiayuan Zhou, Ruiqi Pan +1
Although LLM-driven repair agents can tackle complex, repository-level issues, they treat every issue independently and discard the procedural knowledge accumulated from previous r…
Preventing Premature Commitment in Coding Agents with an Evidence-Conditioned Execution Layer
Yisen Xu, Chenglin Li, Zehao Wang +2
LLM-based coding agents often edit source code or submit patches before examining enough repository evidence to justify the change, a failure pattern we call premature commitment.…
Rethinking Code Performance Benchmarks for LLMs
Nhat Minh Le, Yisen Xu, Zhijie Wang +2
Many function-level performance benchmarks have been proposed to evaluate whether large language models (LLMs) can generate efficient programs. However, results on these benchmarks…
From Historical Patches to Repair Plans: Outcome-Conditioned Reasoning for Repository-Level Program Repair
Chenglin Li, Yisen Xu, Zehao Wang +3
Repository-level automated program repair (APR) requires long-horizon reasoning over interdependent decisions. However, most LLM-based approaches reconstruct repair reasoning indep…