mlsanityData to 14 Oct 2026

Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents

arXiv 2610.18684 ↗First posted 13 Oct 2026cs.AI cs.HC cs.LGcited by 0

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Abstract (arXiv)

Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing what the user and the agent have established together. An assistant's proposal should not become an authorized instruction merely because it is remembered. We introduce Grounding Awareness, the ability to distinguish accepted, unacknowledged, revised, and scoped arrangements when selecting actions. We develop a controlled benchmark to test whether agents can tell the difference between proposals users have accepted and those they have not, and whether they apply accepted arrangements only where appropriate. Across multiple agents and memory systems, we find that strong memory performance does not ensure Grounding Awareness. More surprisingly, providing more complete user information can increase execution of unacknowledged proposals, while persistent memory can cause agents to treat these proposals as if they were user instructions. We trace these failures through memory writing, retrieval, and action selection. Finally, we introduce Grounding Layer, a lightweight, training-free extension that explicitly tracks grounding status and checks consequential actions, substantially improving agent behavior without modifying models or memory systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 13 Oct 2026.

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23 of 42 references.

References · 23

  1. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  2. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  3. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
  4. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
  5. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  6. 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
  7. 2025Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scalecited by 178
  8. 2023FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactionscited by 171
  9. 2025Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMscited by 148
  10. 2024Hello Again! LLM-powered Personalized Agent for Long-term Dialoguecited by 123
  11. 2025In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agentscited by 106
  12. 2024Towards Lifelong Dialogue Agents via Timeline-based Memory Managementcited by 51
11 more
  1. 2023Grounding Gaps in Language Model Generationscited by 47
  2. 2020Grounding Conversations with Improvised Dialoguescited by 22
  3. 2025CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactionscited by 22
  4. 2026GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversationscited by 17
  5. 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Unitscited by 9
  6. 2025From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactionscited by 8
  7. 2024SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Scriptcited by 7
  8. 2026MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversationscited by 7
  9. 2024Towards Harnessing Large Language Models for Comprehension of Conversational Groundingcited by 3
  10. 2026OpenJarvis: Personal AI, On Personal Devicescited by 3
  11. 2026Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogscited by 2

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