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The paper proposes a neuro-symbolic framework that decouples reasoning into symbolic validity and semantic groundedness, using a verifier and a trained PRM to improve reliability in scientific reasoning tasks for LLMs.
This paper introduces LogMILP, a weakly-supervised framework for log instance anomaly localization that uses prototype-guided structural modeling and counterfactual perturbation consistency regularization to improve detection and interpretability with only bag-level labels.