DissProve demonstrates automated safety verification for parametric asynchronous distributed protocols classified as affine by backward unreachability using novel materialization, causality, and summarization.
McMillan, Oded Padon, Mooly Sagiv, Sharon Shoham, James R
4 Pith papers cite this work. Polarity classification is still indexing.
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LORIS detects local reasoning errors in LLM-generated proofs for loop invariants by translating natural-language steps to first-order logic implications and using invalid implications to refine the invariants, achieving 93.1% success on 460 C programs.
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
MileStone models compiler phase ordering as a multi-objective optimization problem using graph representations, GNN predictions, and RL agents to find Pareto-optimal pass sequences under user constraints.
citing papers explorer
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DissProve: Automated Verification of Distributed Protocols with Affine Communication
DissProve demonstrates automated safety verification for parametric asynchronous distributed protocols classified as affine by backward unreachability using novel materialization, causality, and summarization.
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Guiding LLM-based Loop Invariant Synthesis via Feedback on Local Reasoning Errors
LORIS detects local reasoning errors in LLM-generated proofs for loop invariants by translating natural-language steps to first-order logic implications and using invalid implications to refine the invariants, achieving 93.1% success on 460 C programs.
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SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
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MileStone: A Multi-Objective Compiler Phase Ordering Framework for Graph-based IR-Level Optimization
MileStone models compiler phase ordering as a multi-objective optimization problem using graph representations, GNN predictions, and RL agents to find Pareto-optimal pass sequences under user constraints.