NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
DIFFBASE: a differential factbase for effective software evolution management
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RAVEN combines agentic RAG, iterative repair, and a cross-file Curator Agent to achieve 83.13% repair success on diverse real-world CVEs using local open-source LLMs.
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NESA: Relational Neuro-Symbolic Static Program Analysis
NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.