AI RESEARCH
Learning Multi-Agent Coordination via Sheaf-ADMM
arXiv CS.LG
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ArXi:2605.31005v1 Announce Type: new We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by an agent that solves a convex subproblem parameterized by a neural encoder. Agents coordinate through the Alternating Direction Method of Multipliers (ADMM) with inter-agent constraints specified by a cellular sheaf. The sheaf specifies which aspects of neighboring solutions must agree, allowing for heterogeneous notions of global consensus.