Multi-User Large Language Model Agents

Key points

Large language models and their agents are optimized mainly for single-actor interaction scenarios, which overlooks distinct user identities, roles, and potentially conflicting goals.
Existing LLM agent systems lack native protocols for distinguishing user roles, enforcing information boundaries, or resolving conflicts among multiple users' interests.
Current models struggle to manage the heterogeneous utility, different permission levels, information asymmetry, and privacy constraints embedded in realistic team workflows and organizational tools.
The authors formalize multi-user LLM interaction as a multi-actor decision problem in which a single agent optimizes a weighted social objective while accounting for each user's utility, permissions, and access-control policy.
They establish a unified interaction protocol that defines user personas with explicit permissions and private context, along with agent mechanisms that observe shared context and provide personalized updates.
To empirically evaluate modern LLMs on multi-actor tasks, they design a suite of three stress-test scenarios: multi-user instruction following, user-to-user access control, and multi-user meeting coordination.

External research summaries. These are not HDATF publications or measured product results.