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AdaRepair-Mem introduces an adaptive experience retrieval framework for repository-level program repair, addressing three key limitations in existing memory-augmented methods: imbalanced episodic memory, non-monotonic success with increased memory, and phase misalignment of memory types. The framework employs coverage-aware retrieval, quality-aware selection, and stage-aware routing to better match repair contexts with relevant memories. Evaluations on SWE-Bench-Lite and SWE-Bench-Verified show improved performance on under-covered repositories, reduced noisy retrieval, and enhanced patch refinement support.
arXiv:2609.23570v1 Announce Type: cross Abstract: Coding agents operate on real repository coding tasks, and persistent memory systems promise to reuse experience across tasks. Yet existing evaluatio...
arXiv:2609.01601v1 Announce Type: cross Abstract: The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repo...
arXiv:2605. 17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program repair (APR).
arXiv:2607. 25873v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent.
arXiv:2608. 10502v1 Announce Type: new Abstract: Persistent memory lets language-model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, tool use, answers, and subsequent memory writes.