MAGIC sequentially constructs a collaboration graph by choosing a functional role, implementing that role as either one agent or a reusable group, and connecting the resulting unit to the existing organization.
Mixed-granularity agent graphs improve collaboration across varied tasks
MAGIC learns task-specific organizations that combine individual agents and reusable groups while optimizing both information flow and execution cost.
Independent
Kairui Yang · Ziheng Yi · Xunkai Li · Minghao An · Zhanke Liu · Zekai Chen · +1 more
Research Digest··2 min read
Thread:Multi-Agent Coordination
Yang et al.
Why this paper
Independent · Part of Multi-Agent Coordination, now 24 papers
In one line
MAGIC constructs mixed-granularity agent graphs with dense-reward reinforcement learning and outperforms state-of-the-art baselines across eight benchmarks.
What we could check
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- ·No stated limitations found
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