What Must Generalist Agents Remember?
A new paper develops a formal account of memory requirements for generalist AI agents, proving that near-optimal performance across multiple environments requires storing domain-specific information beyond current observations. The research shows that memory serves as a substrate for domain disambiguation and transition model reconstruction.
[2606.18746] What Must Generalist Agents Remember?
[Submitted on 17 Jun 2026]
Title:What Must Generalist Agents Remember?
View a PDF of the paper titled What Must Generalist Agents Remember?, by Khurram Yamin and 5 other authors
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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. 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)
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