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This paper presents a method for distilling answer-set programming rules from large language models to enhance neurosymbolic visual question answering, showing that only a few examples are needed to generate correct rules.
This paper proposes Geodesic Flow Matching, a Riemannian transport method for denoising Spatial Semantic Pointers (SSPs) on toroidal manifolds, and demonstrates a 72% reduction in tracking error and 40% efficiency gain in a spiking neural SLAM system.
ImProver 2 is a neurosymbolic framework for automated proof optimization in Lean 4 that uses an expert-iteration pipeline and a scaffold to train a 7B-parameter model, outperforming much larger models and demonstrating that small models can effectively restructure research-level proofs.
NeuroNL2LTL is a neurosymbolic framework that translates natural language to Linear Temporal Logic (LTL) using a two-stage architecture with verifier-in-the-loop training, achieving improved correctness guarantees for safety-critical specifications.
This paper introduces novel methods for generating high-quality embeddings for Horn logic reasoning using triplet loss, including techniques for balanced training example generation and hard example emphasis, which improve the efficiency of downstream logical reasoning.
This paper introduces Constrained Diffusion for Code (CDC), a training-free neurosymbolic inference framework that integrates constraint satisfaction directly into the reverse denoising process of discrete diffusion models for code generation. CDC consistently improves constraint satisfaction in functional correctness, security, and syntax across benchmarks, outperforming existing diffusion and autoregressive baselines.