标题： 桶中智能体：代理沙盒化与可解释性高效世界建模的基本限制 显示英文标题

标题： AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability

摘要： 近期的研究提出利用世界模型生成可控的虚拟环境，在将人工智能代理部署之前对其进行测试，以确保其可靠性和安全性。然而，准确的世界模型通常具有较高的计算需求，这可能会严重限制此类评估的范围和深度。受到经典的“缸中之脑”思想实验的启发，我们在此研究简化世界模型的方法，这些方法对正在评估的人工智能代理保持无偏性。通过遵循计算力学的原则，我们的方法揭示了世界模型构建过程中效率与可解释性之间的基本权衡，表明没有任何单一的世界模型能够优化所有期望的特性。基于这一权衡，我们确定了构建世界模型的程序，这些模型要么最小化内存需求，要么划定可学习内容的边界，或者允许追踪不良结果的原因。通过这样做，这项工作确立了世界建模的基本限制，并提出了指导有效代理评估的核心设计选择的可行指南。 显示英文摘要

摘要： Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment to ensure their reliability and safety. However, accurate world models often have high computational demands that can severely restrict the scope and depth of such assessments. Inspired by the classic `brain in a vat' thought experiment, here we investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation. By following principles from computational mechanics, our approach reveals a fundamental trade-off in world model construction between efficiency and interpretability, demonstrating that no single world model can optimise all desirable characteristics. Building on this trade-off, we identify procedures to build world models that either minimise memory requirements, delineate the boundaries of what is learnable, or allow tracking causes of undesirable outcomes. In doing so, this work establishes fundamental limits in world modelling, leading to actionable guidelines that inform core design choices related to effective agent evaluation.