Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2609.11737.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

ORCH applies human organizational theory to multi-agent AI, constructing task-specific hierarchical organizations that combine pooled interdependence (concurrent work) with sequential interdependence (prerequisite-ordered work). Tested across 25 wildfire-response missions with up to 50 embodied agents and 8 LLMs, it outperformed four baseline frameworks by 63.97% on final score and 74.29% on execution efficiency.

Translates organizational theory into concrete coordination rules for heterogeneous agent teams, with measured gains across missions and model scales. Relevant for anyone structuring multi-agent systems beyond flat orchestration.

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