MemRepair is a hierarchical memory-augmented agent framework that raises repository-level vulnerability repair rates to 58.0-58.2% on Python/Go/JS benchmarks and 30.58% on C++ by combining history, pattern, and refinement memories with iterative feedback.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.SE 5years
2026 5representative citing papers
AOCI creates an incremental symbolic-semantic index per code unit that gives LLMs a complete, consistent repository view, outperforming baselines with zero defects on 19 industrial tasks while using far fewer tokens.
GALA uses hierarchical graph alignment between UI screenshots and code structures to achieve state-of-the-art bug localization in multimodal automated program repair on SWE-bench.
ContraFix uses contrastive runtime analysis plus a dual-track skill base to reach 92% resolution on SEC-Bench and 73.8% on PatchEval while improving semantic correctness of patches.
Auto-Diagnose applies LLMs to summarize and diagnose root causes of integration test failures, reporting 90.14% accuracy on 71 manual cases and positive adoption after Google-wide rollout.
citing papers explorer
-
MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair
MemRepair is a hierarchical memory-augmented agent framework that raises repository-level vulnerability repair rates to 58.0-58.2% on Python/Go/JS benchmarks and 30.58% on C++ by combining history, pattern, and refinement memories with iterative feedback.
-
AOCI: Symbolic-Semantic Indexing for Practical Repository-Scale Code Understanding with LLMs
AOCI creates an incremental symbolic-semantic index per code unit that gives LLMs a complete, consistent repository view, outperforming baselines with zero defects on 19 industrial tasks while using far fewer tokens.
-
GALA: Multimodal Graph Alignment for Bug Localization in Automated Program Repair
GALA uses hierarchical graph alignment between UI screenshots and code structures to achieve state-of-the-art bug localization in multimodal automated program repair on SWE-bench.
-
ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
ContraFix uses contrastive runtime analysis plus a dual-track skill base to reach 92% resolution on SEC-Bench and 73.8% on PatchEval while improving semantic correctness of patches.
-
LLM-Based Automated Diagnosis Of Integration Test Failures At Google
Auto-Diagnose applies LLMs to summarize and diagnose root causes of integration test failures, reporting 90.14% accuracy on 71 manual cases and positive adoption after Google-wide rollout.