Tag
This paper presents a homotopy-type-theoretic generalization of neurosymbolic inference that preserves symmetry information and proof multiplicity, showing that this framework recovers classical inference when symmetries are trivial and yields shortcut-aware concept posteriors computable in closed form, with practical improvements on reasoning-shortcut benchmarks.
This position paper proposes the TRISM framework that integrates NeuroSymbolic AI with LLMs and RAG to address hallucination and interpretability issues in legal AI, introducing RASOR RAG for generating interpretable rationales and formalizing symbolic legal knowledge bases.
This paper accelerates the NeurASP neurosymbolic AI framework by implementing vectorization, batch processing, and caching, achieving multiple orders of magnitude speedup on larger tasks.
This paper presents a systematic human audit of NL-to-FOL datasets FOLIO and MALLS, finding 39% and 36% incorrect formalizations respectively. It releases corrected ground truths and an LLM-assisted framework to focus human relabeling, reducing the review workload to under 24% of instances for 90% accuracy.
This paper provides an algebraic formalization of the Theory of Dyadic Morality using structural causal modeling, and demonstrates applications to AI policy design.
This paper introduces LANTERN, a framework for multi-source neurosymbolic transfer in reinforcement learning that uses LLMs to generate task automata and adaptive gating to improve sample efficiency.