teams-up-50-heterogeneous-embodied-llm-agents-simulated-wildfire CLM-0493For teams of up to 50 heterogeneous embodied LLM agents in simulated wildfire missions, building the manager hierarchy per mission so that concurrently executable work sits under a parallel-allocating manager and prerequisite-chained work under a phase-advancing manager beats fixed decentralized, single-planner, and hybrid coordination architectures on mission score and on progress accumulated per step, and the advantage does not depend on which base language model is used.
Capability: Getting more from several models than from one
Observed on
25 simulated wildfire-response missions in an extended CREW-Wildfire benchmark, 3-50 agents of four types, eight base LLMs, five seeds; hierarchy fixed before execution and not res.
Sources
- Validated by: Against four prior embodied multi-agent frameworks (CAMON, COELA, HMAS-2, Embodied) on 25 missions x 8 LLMs x 5 seeds: human-designed hierarchies improved final score 63.97% and execution efficiency (AUC of progress) 74.29% on average, LLM-generated ones 43.63% and 52.53%; first-place rank frequency 32.6% vs 5.8-16.2% for baselines; Type-II ANOVA showed a significant algorithm main effect and no significant algorithm-by-LLM interaction; success rates 35.2-53.1% vs 6.9-13.5%.. [truncated]
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- Splitting a task across several agents helps on long-horizon tasks whose parts depend on each other sparsely, and hurts on tightly coupled sequential workflows, where generic multi-agent methods fall below a single-agent harness because decomposition fragments the dependency chain and coordination overhead outweighs the context isolation it buys; as single agents improve the collaboration benefit shrinks.Getting more from several models than from one · reviewed by AI
- For CAE simulation tasks (OpenFOAM, FEniCS, PyChrono, COMSOL) run with a current general-purpose coding-agent harness and a strong backbone, a single agent with no multi-agent role decomposition and no scripted self-critique matches or exceeds the published scores of multi-agent specialized CAE systems, because the harness itself supplies the multi-turn reasoning and execution-feedback repair the decomposition was built to compensate for.Getting more from several models than from one · reviewed by AI
- In multi-agent literature-synthesis pipelines, most of the gain over a fixed role-based pipeline comes from the shared-workspace architecture and the added critique/dissemination actions rather than from learning the coordination policy: on OARelatedWork, a learned decentralized RL controller scored 3.82 overall versus 3.80 for a centralized LLM coordinator given the same workspace and action space, while a naive orchestrator without that architecture scored 3.40.Getting more from several models than from one · reviewed by AI
- For LLM agents that must compose and then execute domain tool workflows, training separate adapters for the two roles splits the gains cleanly: supervised fine-tuning of the planner on reference tool sequences mainly improves workflow-order fidelity, while node-level RL on argument-match rewards mainly improves tool-argument grounding and final-answer quality, and combining both beats either on a 9B backbone.Planning a sequence of actions before acting · reviewed by AI
- When initially identical LLM agents act in a shared persistent world where their artifacts remain and are scored by an external simulator, the shared society builds broader and more resilient technology portfolios than a matched best-of-N population of isolated agents, but isolated search still yields the single best-performing artifact.Getting more from several models than from one · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A replication in which the same worker agents and base models under a fixed flat or single-planner architecture match or beat the interdependence-matched hierarchy on mission score and progress-per-step, or in which the gain appears only for some base models. Drafted stance toward splitting-task-across-several-agents-helps-long-horizon-tasks-whose: supports -- Both find that whether multi-agent decomposition helps depends on the dependency structure of the task, and that matching structure to dependencies is where the gain comes from.