PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
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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.
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.
PAFT improves LLM-based program repair pass rates by up to 65.6% while cutting average edit distance by up to 32.6% through explicit preservation signals and curriculum training.
TraceRepair deploys a probe agent for runtime snapshots and a committee of agents for cross-verification to fix 392 defects on Defects4J, outperforming prior LLM-based automated program repair methods.
citing papers explorer
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Generating Proof-of-Vulnerability Tests to Help Enhance the Security of Complex Software
PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
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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.
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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.
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PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair
PAFT improves LLM-based program repair pass rates by up to 65.6% while cutting average edit distance by up to 32.6% through explicit preservation signals and curriculum training.
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Runtime Execution Traces Guided Automated Program Repair with Multi-Agent Debate
TraceRepair deploys a probe agent for runtime snapshots and a committee of agents for cross-verification to fix 392 defects on Defects4J, outperforming prior LLM-based automated program repair methods.