将复杂世界模型拆解为模块化组件，提升推理效率与可解释性。
From monoliths to modules: Decomposing transducers for efficient world modelling
- 通过逆向分解传输器模型，提取独立输入输出子空间的子模块。
- 实现并行化推理，降低计算开销，保持模型结构透明。
- 适合关注AI安全与高效部署的研究者和工程师。
世界模型被提出作为训练和评估AI代理的沙盒环境。尽管真实世界模型通常计算开销大，但可通过利用现实场景中各子组件以模块化方式交互的特性来缓解。本文提出一种框架，将由传输器（transducers）表示的复杂世界模型进行分解，传输器是广义马尔可夫决策过程（POMDPs）的模型类。虽然传输器的组合机制已有明确定义，但其逆向分解仍不清晰。本研究明确了如何推导出在不同输入-输出子空间上运行的子传输器，从而实现可并行、可解释的世界建模替代方案，支持分布式推理。这些成果为弥合真实世界推理所需的计算效率与人工智能安全要求的结构透明性之间提供了基础。
原文摘要 · Abstract (English)
World models have been recently proposed as sandbox environments in which AI agents can be trained and evaluated before deployment. While realistic world models often have high computational demands, this can often be alleviated by exploiting the fact that real-world scenarios tend to involve subcomponents that interact in a modular manner. In this paper, we explore this idea by developing a framework for decomposing complex world models represented by transducers, a class of models generalising POMDPs. Whereas the composition of transducers is well understood, our results clarify how to invert this process by deriving sub-transducers operating on distinct input-output subspaces, enabling parallelizable and interpretable alternatives to monolithic world modelling that can support distributed inference. Overall, these results lay groundwork for bridging the computational efficiency required for real-world inference and the structural transparency demanded by AI safety.
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