Rohan Paul@rohanpaul_ai · X该论文指出，通用智能体不能仅依赖当前观测，必须记住隐藏环境规则。当两个隐藏域在相同可见状态下要求相反动作时，仅凭观察无法区分当前场景。作者证明，要在两个域都表现良好的智能体，必须为不同域维持不同的内部记忆状态。核心结论：好的通用智能体不是对当前所见做出反应，而是必须携带来自先前经验的隐藏上下文。
This paper shows that a good generalist agent must remember hidden environment rules， not just observe the current state.
That sounds obvious until you notice the trap this paper isolates： two worlds can show the agent the same state， offer the same goal， and still require opposite actions.
At that moment， observation is no longer enough.
The important object is not "memory" as a vague engineering feature， but memory as the place where hidden context must be carried when the environment refuses to label itself.
The paper's core idea is that memory is not optional in this setting， because a near-perfect agent must store enough past experience to tell which hidden environment it is currently in.
The authors prove that when 2 hidden domains require incompatible actions at the same visible state， any agent that performs well across both domains must have different internal memory states for those domains.
The big point is that good generalist agents do not just react to what they see now， because they must carry hidden context from earlier experience when the world can change underneath the same observation.
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Link - arxiv. org/abs/2606.18746
Title： "What Must Generalist Agents Remember？"