The paper introduces ORCH, a method that applies human organizational theory to create task‑specific hierarchical structures for large, heterogeneous embodied AI teams. Using ORCH, teams of up to 50 agents across 25 wildfire‑response missions outperformed four existing multi‑agent frameworks, achieving higher mission scores and greater execution efficiency. Both human‑designed and language‑model‑generated ORCH organizations improved performance, with hierarchical organization preserving concurrent activity while coordinating ordered transitions between mission phases.
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...
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...
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.
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.
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...
arXiv:2609.22682v1 Announce Type: new Abstract: Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unkno...
SwarmWorld demonstrates that homogeneous language‑model agents can self‑organize into evolving technological societies without assigned roles or direct communication. In a spatial environment, agents explore, process resources, construct artifacts, and write executable controllers that are later evaluated by a deterministic simulator. The resulting societies develop broader, more resilient technological portfolios than isolated search, with agents differentiating into exploration, construction, maintenance, and coordination roles as the world matures.
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
arXiv:2607. 25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol).
arXiv:2607. 05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons.
arXiv:2606. 31966v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored.