mlsanityData to 7 Oct 2026

Keep It InMind: Benchmarking the Implicit-Association Blind Spot in Agent Memory

arXiv 2607.24368 ↗First posted 27 Jul 2026cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Long-term memory systems store what a user says in an external store and retrieve it when a related query arrives. This interface rests on an assumption so natural that it is rarely stated: a memory that is needed will resemble the query that needs it. World knowledge breaks the assumption. A tree-nut allergy should change the answer to a macaron request through their almond-flour ingredient, yet the two texts share no cue a retriever can see. We call this failure mode the implicit-association blind spot and introduce InMind, a 125-task, expert-verified benchmark spanning ten life domains, with 113 tasks grounded in citable public sources. Its paired controls separate three explanations that existing evaluations conflate: the fact was never stored, the model lacks the bridging knowledge, or the fact was stored and never surfaced. The verdict is clean. With the decisive memory placed in context, the backbone answers 84.0 percent of indirect queries; when the same memory must be retrieved, six vector, graph, and agentic memory systems reach at most 14.4 percent, even though they recall the same facts on demand at up to 100 percent. An embedding with eight times the dimensionality raises answer-blind target recall for every system yet leaves the gap essentially intact. A minimal diagnostic probe that keeps memory visible before the query arrives recovers most of the gap, locating the failure in the query-conditioned interface itself and pointing to routing, deciding which facts must stay visible, as the open problem InMind is built to score.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 63 references that are in the map. First posted on arXiv 27 Jul 2026.

Builds on

34 of 63 references.

References · 34

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22 more
  1. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
  2. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
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  15. 2025On Memory Construction and Retrieval for Personalized Conversational Agentscited by 101
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Madeleine: Learning Involuntary Recall for Conversational Memory from Simulated Livescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects
  2. 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
  3. 2026Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History
  4. 2026Learning What to Remember: A Cognitively Grounded Multi-Factor Value Model for Agentic Memory
  5. 2026Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
  6. 2026MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
  7. 2026Evaluating Memory Structure in LLM Agents
  8. 2026Storage Is Not Memory: A Retrieval-Centered Architecture for Agent Recall