A logic-based verifier using temporal annotated logic raises the share of LLM-proposed vulnerability exploration paths that satisfy domain knowledge from 78% to 98%.
Turning 30: New Ideas in Inductive Logic Programming
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abstract
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.
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2026 1verdicts
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EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming
A logic-based verifier using temporal annotated logic raises the share of LLM-proposed vulnerability exploration paths that satisfy domain knowledge from 78% to 98%.