NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoning

• URL: http://arxiv.org/abs/2510.19429v1
• Date: Wed, 22 Oct 2025 09:57:02 GMT
• Title: NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoning
• Authors: Wonje Choi, Jooyoung Kim, Honguk Woo,
• Abstract summary: NeSyPr is a novel embodied reasoning framework that compiles knowledge via neurosymbolic proceduralization.<n>It supports efficient test-time inference without relying on external symbolic guidance.<n>We evaluate NeSyPr on the embodied benchmarks PDDLGym, VirtualHome, and ALFWorld.
• Score: 21.685443540926652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenge of adopting language models (LMs) for embodied tasks in dynamic environments, where online access to large-scale inference engines or symbolic planners is constrained due to latency, connectivity, and resource limitations. To this end, we present NeSyPr, a novel embodied reasoning framework that compiles knowledge via neurosymbolic proceduralization, thereby equipping LM-based agents with structured, adaptive, and timely reasoning capabilities. In NeSyPr, task-specific plans are first explicitly generated by a symbolic tool leveraging its declarative knowledge. These plans are then transformed into composable procedural representations that encode the plans' implicit production rules, enabling the resulting composed procedures to be seamlessly integrated into the LM's inference process. This neurosymbolic proceduralization abstracts and generalizes multi-step symbolic structured path-finding and reasoning into single-step LM inference, akin to human knowledge compilation. It supports efficient test-time inference without relying on external symbolic guidance, making it well suited for deployment in latency-sensitive and resource-constrained physical systems. We evaluate NeSyPr on the embodied benchmarks PDDLGym, VirtualHome, and ALFWorld, demonstrating its efficient reasoning capabilities over large-scale reasoning models and a symbolic planner, while using more compact LMs.

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