A neuro-symbolic framework reconstructs semantics from opaque binaries via abstract interpretation, reflexive LLM prompting, typed knowledge graphs, and Graphormer reasoning to outperform baselines in vulnerability detection and APT matching for industrial control systems.
Software: Practice and Experience , volume=
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cs.SE 2years
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GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.
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Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software
A neuro-symbolic framework reconstructs semantics from opaque binaries via abstract interpretation, reflexive LLM prompting, typed knowledge graphs, and Graphormer reasoning to outperform baselines in vulnerability detection and APT matching for industrial control systems.
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Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
GLMTest integrates code property graphs and GNNs with LLMs to steer test case generation toward targeted branches, raising branch accuracy from 27.4% to 50.2% on the TestGenEval benchmark.