mlsanityData to 14 Oct 2026

Remember, Verify, or Ask? Cross-Family Evaluation of Memory Commitment in LLM Agents

arXiv 2608.19564 ↗First posted 20 Aug 2026cs.CLcited by 2

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

Abstract (arXiv)

Persistent memory can personalize an LLM agent, but an incorrect durable update can silently distort future behavior. We study the memory-clarification boundary: whether interaction-derived information should be persisted, used only in the current context, re-verified, or clarified with the user. MCB contains 140 primary scenarios, split into 70 development and 70 held-out items, plus a separate 70-item contrast set. It evaluates both action labels and structured tool-call selection. Two non-authors independently label the 70 held-out primary and 70 contrast items (97.1% agreement, Cohen's kappa = 0.962); a blind third resolves four disagreements, replacing eight author labels by non-author majority. Across Claude and Qwen, models verify changing facts more reliably than they ask users to resolve ambiguity. Bare Qwen asks on 0/12 clarification items while verifying 12/18 freshness items. Few-shot prompting raises accuracy from 0.557 to 0.771 (paired delta = +0.214, Holm-adjusted exact McNemar p_H = 0.002), yet clarification recall remains 0.333. The policy prompt reduces erroneous persistence from 0.243 to 0.100 (p_H = 0.038), although its accuracy gain is not significant. Label-tool agreement is 57% for each Claude model and 23% for Qwen; Qwen accuracy falls from 0.557 to 0.343 (p_H = 0.047). Memory evaluation must test both stated decisions and tool-call choices.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 15 of its 18 references that are in the map. First posted on arXiv 20 Aug 2026.

Builds on

15 of 18 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents25 May
  2. 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents21 Apr
  3. 2026Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents13 Jan
  4. 2025Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning27 Aug
  5. 2025MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents20 Jun
  6. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
  7. 2024Evaluating Very Long-Term Conversational Memory of LLM Agents27 Feb

Other references · 8

  1. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  2. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  3. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
  4. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 627
  5. 2023Clarify When Necessary: Resolving Ambiguity Through Interaction with LMscited by 98
  6. 2024CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Modelscited by 54
  7. 2024Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questionscited by 32
  8. 2026AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignmentcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026A Benchmark and Diagnostic Study of Epistemic Admission in Shared Agent Memorycited by 1
  2. 2026Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memorycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary
  2. 2026Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents
  3. 2025How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior
  4. 2026Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell
  5. 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?
  6. 2026Memory Is a Derivation: The Distributed-Evidence Paradox in Long-Term Agents
  7. 2026Evaluating Memory Structure in LLM Agents
  8. 2026The Epistemics of Agent Memory: Measuring, and Governing, the Consolidation Decision in Long-Horizon LLM Agents