newsNature Machine IntelligencePublished yesterdayLive · 21h ago
A collaborative agent with two lightweight synergistic models for autonomous crystal materials research
Nature Machine Intelligence, Published online: 10 September 2026; doi:10.1038/s42256-026-01298-6 Shi et al. demonstrate a dual architecture approach for materials research, integrating two lightweight large language models for collaborative reasoning and scientific tool execution. The method achieves competitive performance while remaining affordable and locally deployable.
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paperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual RecombinationpaperAgents in the Wild: Where Research Meets DeploymentpaperAuto Research for Materials: Auditable AI-Scientist Workflows with Held-Out TransferrepoInternScience/ResearchClawBenchpaperBioinfoysis Technical Report
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paperBioinfoysis Technical ReportpaperAgents in the Wild: Where Research Meets DeploymentrepoInternScience/ResearchClawBenchpaperGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual RecombinationpaperAuto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer