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  "text": "// Multi-User LLM Agents //\n\nEvery agent framework assumes one user giving instructions. But deploy an agent into a team workflow, and suddenly it has multiple bosses with conflicting goals, private information, and different authority levels.\n\nThis work formalizes multi-user interaction as a multi-principal decision problem and introduces Muses-Bench with three scenarios: instruction following under authority conflicts, cross-user access control, and multi-user meeting coordination.\n\nEven the best model, Gemini-3-Pro, only averages 85.6% across tasks. On meeting coordination, no model exceeds 64.8% success rate. Privacy-utility tradeoffs are especially brutal: models that score near-perfect on privacy (Grok-3-Mini at 99.6%) tank on utility (60.1%).\n\nWhy does it matter?\n\nAs agents move into organizational tools, Slack bots, and shared workspaces, multi-principal conflicts become the default, not the exception. Current models aren't ready. They leak more privacy over multi-turn interactions and can't maintain stable prioritization under conflicting objectives.\n\nPaper: https://t.co/ttoFSlIYxC\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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