mlsanityData to 7 Oct 2026

Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations

arXiv 2606.00832 ↗First posted 30 May 2026cs.CLcited by 1

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 17 of its 31 references that are in the map. First posted on arXiv 30 May 2026.

Builds on

17 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025TReMu: Towards Neuro-Symbolic Temporal Reasoning for LLM-Agents with Memory in Multi-Session Dialogues3 Feb
  2. 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memory20 Jan

Other references · 15

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  4. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  5. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
  6. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  7. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
  8. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
  9. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 407
  10. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 306
  11. 2024WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisinescited by 39
  12. 2025AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulationcited by 21
3 more
  1. 2025Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Usecited by 16
  2. 2025T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planningcited by 14
  3. 2026SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discoverycited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistantscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents
  2. 2026AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
  3. 2026Agents in the Large: Perception-Centered Architecture for Persistent Agents
  4. 2026MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
  5. 2026SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent
  6. 2026MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
  7. 2026MemGym: a Long-Horizon Memory Environment for LLM Agents
  8. 2026VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions