arXiv:2609.11737v2 Announce Type: replace-cross
Abstract: Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artifici...
By Zhengran Ji, Jonathan Hyun, Boyuan Chen
The paper introduces ORCH, a method that applies human organization theory to create task‑specific hierarchical structures for large, heterogeneous embodied AI teams. By combining concurrent and sequential interdependence, ORCH outperforms four existing multi‑agent frameworks across 25 wildfire‑response missions, improving mission outcomes, execution efficiency, exploration, and computational resource use. Both human‑designed and language‑model‑generated ORCH organizations yield significant performance gains, with hierarchical organization enabling sustained concurrent activity and coordinated transitions in long‑horizon missions.
arXiv:2510.26915v2 Announce Type: replace-cross
Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large...
By Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Carlos Nieto-Granda, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar
AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
By Jiabin Lou, Yirong Yang, Haopeng Wang, Xuxin Lv, Xinyu Liu, Diyuan Hou, Xuehong Liu, Rongye Shi, Wenjun Wu
Agensh is a new multi‑agent harness that eliminates a central orchestrator by letting workers self‑organize through a continuous cooperation loop. The system uses a shared workspace, message interface, and shared context to coordinate tasks, verify results, and merge progress asynchronously. Experiments on ProgramBench and pandoc show that scaling from 1 to 1,024 agents improves test‑pass rates by up to 49% relative, demonstrating that agent count is a viable scaling dimension for complex tasks.
By Zhihao Zhan, Ting Song, Li Dong, Shaohan Huang, Jianxun Lian, Yan Xia, Furu Wei
Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody...