What Must Generalist Agents Remember?

通用智能体必须记住什么？

Abstract: This paper develops a formal account of what generalist agents must store in memory in order to act near-optimally across multiple environments and goals. It shows that when two domains share an observational bottleneck but require incompatible optimal actions, any uniformly near-optimal policy must induce distinct memory distributions at that bottleneck.

摘要： 本文对通用智能体为了在多种环境和目标下实现近乎最优的行为而必须存储在内存中的内容进行了形式化描述。研究表明，当两个领域共享一个观测瓶颈但需要不兼容的最优动作时，任何统一的近乎最优策略都必须在该瓶颈处诱导出不同的内存分布。

The result yields a separation theorem: sufficiently successful agents cannot rely only on current state observations, but must preserve domain-relevant information in memory. The paper further shows that if an agent’s memory contains enough information to estimate values for related goals, then that memory can be used to approximately reconstruct the agent’s local transition dynamics.

该结果导出了一个分离定理：足够成功的智能体不能仅依赖当前的观测状态，而必须在内存中保留与领域相关的信息。论文进一步指出，如果智能体的内存包含足够的信息来估计相关目标的价值，那么这些内存就可以用来近似重构智能体的局部转移动态。

Together, these results characterize memory as the substrate that supports domain disambiguation, transition-model reconstruction, and planning for generalist agents.

综上所述，这些结果将内存刻画为支持通用智能体进行领域消歧、转移模型重构和规划的基础基质。