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.

翻译：我们针对在动态环境中采用语言模型（LMs）执行具身任务所面临的挑战，该环境下由于延迟、连接性和资源限制，难以在线访问大规模推理引擎或符号规划器。为此，我们提出NeSyPr，一种新颖的具身推理框架，它通过神经符号程序化来编译知识，从而为基于LM的智能体提供结构化、自适应且及时的推理能力。在NeSyPr中，任务特定规划首先由符号工具利用其声明性知识显式生成。这些规划随后被转化为可组合的程序化表示，以编码规划中隐含的产生式规则，使得最终组合而成的程序能够无缝集成到LM的推理过程中。这种神经符号程序化将多步符号化结构化路径查找与推理抽象并泛化为单步LM推理，类似于人类的知识编译过程。它支持无需依赖外部符号指导的高效测试时推理，使其非常适合部署在对延迟敏感且资源受限的物理系统中。我们在具身基准测试PDDLGym、VirtualHome和ALFWorld上评估NeSyPr，结果表明其在使用更紧凑LMs的同时，相较于大规模推理模型和符号规划器，展现出高效的推理能力。