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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 proposes MONIR, a Modalized-Output Normative Intermediate Representation that bridges LLM-assisted norm extraction and ASP-based compliance reasoning for technical standards. The framework is instantiated on Chinese ADAS regulations, combining symbolic reasoning with LLM pipelines for explainable compliance checking.
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 presents an Answer Set Programming (ASP) based implementation of the CARCASS framework for constructing abstractions in reinforcement learning, demonstrating its effectiveness on Blocks World and Minigrid domains.