Knowledge Curation for Multi-User Collaboration in AI Workspaces

As AI workspaces move from personal, single-user use to shared, team-level projects, they need a memory that captures what a team knows, who can access it, how it was established, and whether it remains valid. Existing memory mechanisms and evaluations largely target storing and retrieving a single user’s long-context history. However, team settings pose a different challenge, as the knowledge is distributed across conversations, meetings, and shared documents, with evolving content and varying visibility. We introduce Team Knowledge Curation (TKC), a framework that represents shared knowledge as scoped items carrying epistemic type, lifecycle state, provenance, and access constraints, curated from raw conversation and maintained through periodic consolidation. To evaluate this setting, which existing benchmarks do not do, we introduce TeamKnowledgeBench (TKB), a benchmark designed to capture team-specific knowledge dynamics and where access denial and abstention are first-class correct answers. Across TKB and an independent GroupMemBench, TKC outperforms existing memory and retrieval systems and is the only system that withholds protected content without leaks, suggesting that reliable collaborative memory requires curating current what the team knows, beyond retrieving what was said. Together, TKC and TKB lay a foundation for AI systems that support shared, evolving team knowledge.