A novel decision procedure for arrays with constant arrays in SMT, supporting any index domain with an abstract calculus, soundness proofs, and implementation in Bitwuzla.
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5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5verdicts
UNVERDICTED 5representative citing papers
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.
Zorya now analyzes gc-compiled Go binaries and detects seven of eleven real-world vulnerabilities at the binary level, including a silent integer overflow missed by other tools without an oracle.
Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.
citing papers explorer
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Satisfiability Modulo Extensional Constant Arrays (Extended Version)
A novel decision procedure for arrays with constant arrays in SMT, supporting any index domain with an abstract calculus, soundness proofs, and implementation in Bitwuzla.
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RAVEN: Agentic RAG for Automated Vulnerability Repair
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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From TinyGo to gc Compiler: Extending Zorya's Concolic Framework to Real-World Go Binaries
Zorya now analyzes gc-compiled Go binaries and detects seven of eleven real-world vulnerabilities at the binary level, including a silent integer overflow missed by other tools without an oracle.
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A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection
Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.
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Improving MPI Error Detection and Repair with Large Language Models and Bug References
Augmenting LLMs with bug references, few-shot learning, chain-of-thought, and RAG improves MPI error detection accuracy from 44% to 77% and generalizes across models.