Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations
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Computer Science > Computation and Language
Title:Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations
Abstract:Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmarks evaluate agents within a single session, ignoring past actions, stated preferences, and prior decisions that agents must integrate to fulfill personalized user goals. We introduce Momento, a benchmark for persistent agentic task completion in multi-session service environments, requiring agents to take consequential, tool-mediated actions while resolving temporal dependencies and evolving user goals across sessions. Experimental results reveal that current agents fail primarily through misestimation of user state, treating prior session history as a reliable proxy for current context rather than stale information requiring re-validation, highlighting a substantial gap between current agent capabilities and realistic long-horizon human-agent interaction.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.00832 [cs.CL] |
| (or arXiv:2606.00832v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.00832
arXiv-issued DOI via DataCite (pending registration)
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