newsNature Machine IntelligenceTrust 88 · LabPublished yesterdayLive · 3m ago
Capable language models can outgrow the benefits of collaboration
Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01268-y A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects the best architecture in 87% of held-out within-domain configurations.
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- PossiblePossibly related (embedding) · 68%Agents in the Wild: Where Research Meets Deployment →
- PossiblePossibly related (embedding) · 62%MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments →
- PossiblePossibly related (embedding) · 62%Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates →
- PossiblePossibly related (embedding) · 61%PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents →
- PossiblePossibly related (embedding) · 61%Gloriaameng/Awesome-Agent-Harness →
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paperAgents in the Wild: Where Research Meets DeploymentpaperMECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied EnvironmentspaperConversable Complexity: Agentic LLM Collectives as Interpretable SubstratespaperPolyWorkBench: Benchmarking Multilingual Long-Horizon LLM AgentsrepoGloriaameng/Awesome-Agent-Harness
Related across the graph
paperPolyWorkBench: Benchmarking Multilingual Long-Horizon LLM AgentsrepoGloriaameng/Awesome-Agent-HarnesspaperMECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied EnvironmentspaperConversable Complexity: Agentic LLM Collectives as Interpretable SubstratespaperAgents in the Wild: Where Research Meets Deployment