Collab AI Research
How Microsoft is using science and research to invent the future of collaboration
Welcome to Collab AI
Collaboration is a central feature of how work gets done, involving coordination across people, tasks, and information sources. As AI systems increasingly contribute to shared workflows—synthesizing discussions, generating alternatives, maintaining context, and supporting group decision processes that unfold across both synchronous and asynchronous interaction—understanding how humans and AI function together in group settings has become a scientific and operational priority. This shift raises new questions about evolving intent, shared grounding, and how collective reasoning unfolds across time and modalities.
Collab AI serves as Microsoft’s research center dedicated to studying collaborative processes and building intelligent systems capable of participating in them. The work spans empirical studies of team interaction, computational models of shared context, dialogic representations that capture how knowledge is co‑created, and methods for enabling AI to engage in multi‑party alignment and decision‑making. Featured papers, talks, and research outputs explore emerging data, architectures, and evaluation frameworks for systems that operate not as single‑user tools, but as contributors to group activity.
Dive in to explore the science behind collaborative AI—and to engage with research aimed at creating intelligent systems that do more than assist individuals. The goal is AI that helps teams form collective purpose, sustain coherent dialogue, and work together more effectively than ever before across conversations, shared and co-authored artifacts, meetings, and organizational workflows.
Actions Speak Louder Than Prompts: Rethinking How LLMs Reason Over Graph Data
We conducted one of the largest controlled evaluations of LLMs for graph inference to date, spanning 14 datasets across four domains, multiple structural regimes, and a range of model sizes and capabilities. The result is a set of practical, actionable insights for people building systems that combine language models with structured data – whether in collaborative platforms, social networks, e-commerce, or beyond.