The Right Memory in the Wrong Context: Verifying Retrieval Admissibility in Long-Term Agent Memory
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Abstract (arXiv)
Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates policy, or reflects an incompatible lifecycle state. Recall and final-answer accuracy do not reveal this: a route can appear safe by missing required evidence, while a correct answer may follow inadmissible prompt exposure. We introduce a retrieval-admissibility verification framework that assigns each memory-query pair one of three statuses (admissible, inadmissible, or unresolved), compares routes at matched required-evidence recall with bounds for unresolved cases, and tracks memory IDs through prompt exposure while linking exposure to target-level disclosure. We evaluate its stages on separate, non-pooled populations. A post-hoc top-20 reanalysis of frozen rankings from two public long-term-memory benchmarks, RHELM and MemOps, covers 3,767 queries. All released anchors lie within trusted query namespaces; with within-namespace scores unchanged, off-namespace filtering cannot lower their ranks. Top-20 anchor recall increases from 0.432 to 0.533, 80% recall feasibility from 0.237 to 0.311, and exact similarity evaluations decrease by 98.3%. In a frozen 72-case development diagnostic, a released-metadata reference preserves required evidence, whereas neither text-only verifier detects violations under the 1% required-anchor false-denial limit. Across 1,523 paired benchmark-native cases, namespace routing is associated with judged-accuracy gains of 0.053-0.068 across three readers; recall also changes, so this comparison is observational. In 16 controlled exposure scenarios, only one of four reader-specific 95% confidence intervals excludes zero for relevant-inadmissible literal disclosure (+0.156, 95% CI [0.031, 0.312]). Results motivate separate verification of candidate support, admissibility, prompt exposure, and answer disclosure.
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References · 16
- 2025Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Controlcited by 61
- 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?cited by 50
- 2026LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleaguescited by 23
- 2026GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agentscited by 13
- 2026Governed Shared Memory for Multi-Agent LLM Systemscited by 13
- 2026MemConflict: Evaluating Long-Term Memory Systems Under Memory Conflictscited by 12
- 2026Beyond Similarity: Trustworthy Memory Search for Personal AI Agentscited by 12
- 2026MAP-Graph: Provenance-Aware Shared Memory for Multi-Agent Workflowscited by 9
- 2026MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversationscited by 7
- 2026RaMem: Contextual Reinstatement for Long-term Agentic Memorycited by 6
- 2026MemAudit: Auditing Long-Term Agent Memory via Hidden User-State Recoverycited by 6
- 2026SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generationcited by 4
4 more
- 2026Selection Integrity for LLM Graph Memory: An Accumulability Criterion for Information-Flow-Blind Retrievalcited by 4
- 2026A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memorycited by 4
- 2026Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memorycited by 2
- 2026MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agentscited by 1
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