{"ID":2862416,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25651","arxiv_id":"2509.25651","title":"AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation","abstract":"The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs achieves near-expert procedural accuracy (F1-score \u003e 0.89) on challenging multi-step syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs","short_abstract":"The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture...","url_abs":"https://arxiv.org/abs/2509.25651","url_pdf":"https://arxiv.org/pdf/2509.25651v1","authors":"[\"Gihan Panapitiya\",\"Emily Saldanha\",\"Heather Job\",\"Olivia Hess\"]","published":"2025-09-30T01:51:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":608893,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2862416,"paper_url":"https://arxiv.org/abs/2509.25651","paper_title":"AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation","repo_url":"https://github.com/pnnl/autolabs","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}