arXiv:2606.18829v1 Announce Type: new 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 contextua
The proliferation of LLM agents into enterprise and shared consumer environments necessitates robust memory management and security protocols, making this a critical area of research as deployment scales.
As AI agents become embedded in shared environments like hospitals and workplaces, secure and governed memory is paramount for privacy, reliability, and preventing misuse, directly impacting trust and adoption.
The focus shifts from single-user agent benchmarks to multi-principal, shared-memory systems, highlighting the need for memory governance alongside mere recall capabilities.
- · AI agent developers focused on security and privacy
- · Organizations deploying multi-user AI systems
- · Cybersecurity firms specializing in AI access control
- · AI agent providers with poor memory governance
- · Users vulnerable to data breaches in shared AI contexts
- · Organizations that neglect multi-user AI security
GateMem will become a standard for evaluating multi-principal AI agent trustworthiness and security.
Increased demand for AI systems with sophisticated access control and memory management features, accelerating their development.
The development of a new 'trust layer' for AI agents, driving specific regulatory frameworks around AI data handling in shared environments.
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Read at arXiv cs.LG