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From the 1 of 9 papers with an AI index.

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cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

Retrieval-Oriented Code Representations in Agentic Bug Localization

Genevieve Caumartin, Tse-Hsun, Chen +1

The paper evaluates how different code representations, including LLM-generated textual summaries, affect the effectiveness and cost of file-level bug localization, finding that ro…

cs.SE2026

Bug Report Specification Refinement with Trajectory Guidance for Automated Program Repair

S M Farah Al Fahim, Md Nakhla Rafi, Md Ahasanuzzaman +5

Bug reports serve as task specifications for repository-level automated program repair (APR) agents, but they often describe only the observed failure and omit repair-relevant info…

cs.SE2026

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…

cs.SE2026

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…