arXiv — NLP / Computation & Language · · 3 min read

GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents

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Computer Science > Machine Learning

arXiv:2606.18829 (cs)
[Submitted on 17 Jun 2026]

Title:GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents

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Abstract:Memory benchmarks for LLM agents largely assume single-user settings, leaving shared assistants for hospitals, workplaces, campuses, and households understudied. In these deployments, multiple principals write to a common memory pool and query it under different roles, scopes, and relationships, so memory quality requires governance as well as recall. We introduce GateMem, a benchmark for multi-principal shared-memory agents. GateMem jointly evaluates utility for legitimate long-horizon requests with state updates, access control across contextual authorization boundaries, and agent-facing active forgetting after explicit deletion requests. It spans medical, office, education, and household domains, with long-form multi-party episodes, incremental memory injection, hidden checkpoints, structured judging, and leak-target annotations. Across diverse baselines and backbone models, no method simultaneously achieves strong utility, robust access control, and reliable forgetting. Long-context prompting often yields the best governance score at high token cost, while retrieval-based and external-memory methods reduce cost yet still leak unauthorized or deleted information. These results show current memory agents remain far from reliable shared institutional deployment.
Comments: 24 pages, 8 figures. Code and dataset are available at this https URL and this https URL
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2606.18829 [cs.LG]
  (or arXiv:2606.18829v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.18829
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhe Ren [view email]
[v1] Wed, 17 Jun 2026 09:06:15 UTC (1,230 KB)
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