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This paper introduces MT-PDCL, a measure-theoretic probabilistic definite clause logic framework that generalizes probabilistic logic programming to continuous domains by using Lebesgue integration over standard Borel σ-algebras instead of discrete grounding. It replaces combinatorial grounding bottlenecks with exact algebraic and differentiable inference while preserving declarative definite clause syntax.
This paper presents a three-stage neural-symbolic pipeline for gaming toxicity detection, combining transformer ensembles with rule-based mediation, achieving top accuracy in the EEUCA 2026 shared task.
This paper introduces NeurOWL, a neuro-symbolic framework using LLMs and ontology embeddings to perform subsumption verification and abduction on incomplete OWL ontologies, enabling reasoning when axioms are missing.
This paper proposes a method to extract an executable Prolog program from a deep reinforcement learning policy, providing theoretical guarantees on return and fidelity, enabling interpretability and manual editing.
Presents FormalAnalyticGeo, a neural-symbolic framework for automatic generation of multimodal analytic geometry problems using a formal intermediate representation (CDL) and LLM components, yielding the AnalyticGeo7K dataset of over 7K verified problems with high geometric precision.
Current mainstream pure data-driven robot solutions suffer from low data efficiency and poor generalization. The newly proposed neuro-symbolic physical intelligence paradigm breaks down tasks into two steps: world modeling and planning. It requires only 1-10 demonstrations to learn new tasks, and its generalization ability far exceeds traditional end-to-end solutions, providing a more reliable path for general-purpose robots.
This paper proposes a new security paradigm for AI agents using a Proof-Constrained Action (ePCA) framework with neural symbolic isolation, achieving zero attack success rate in empirical evaluations.
The paper introduces Chimera Training, a method for logical anomaly detection that uses counterfactual construction at the feature level to train neural rule evaluators without requiring real anomalous images, improving rule-level anomaly detection performance on benchmarks like CLEVRER, OpenImages, and VidOR.