Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
DARS: dynamic action re-sampling to enhance coding agent performance by adaptive tree traversal
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ExpeRepair improves LLM-based repository-level program repair by maintaining episodic memory of concrete fixes and semantic memory of abstract insights, reaching 60.3% and 74.6% pass@1 on SWE-Bench Lite and Verified.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
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Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
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EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
ExpeRepair improves LLM-based repository-level program repair by maintaining episodic memory of concrete fixes and semantic memory of abstract insights, reaching 60.3% and 74.6% pass@1 on SWE-Bench Lite and Verified.
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.