What Must Generalist Agents Remember?
Quick Answer
This paper outlines the memory requirements for generalist agents to perform optimally across varied environments.
Quick Take
It establishes that agents must retain domain-specific information to differentiate actions in overlapping observational bottlenecks, enabling effective planning and transition dynamics reconstruction.
Key Points
- Generalist agents need to store domain-relevant information for optimal performance.
- Observational bottlenecks can lead to incompatible actions requiring distinct memory distributions.
- Memory aids in transition-model reconstruction and planning for various goals.
- Agents must not rely solely on current state observations for effective decision-making.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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. 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. Together, these results characterize memory as the substrate that supports domain disambiguation, transition-model reconstruction, and planning for generalist agents.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18746 [cs.AI] |
| (or arXiv:2606.18746v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18746 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Khurram Yamin [view email]
[v1]
Wed, 17 Jun 2026 06:46:51 UTC (851 KB)
— Originally published at arxiv.org
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