Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra

[2608.14720] Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Physics > Chemical Physics arXiv:2608.14720 (physics) [Submitted on 12 Aug 2026] Title: Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra Authors: Bingsen Xue , Zhuojun Jiang , Jianhao Zhang , Mingcheng Gu , Yizhe Yuan , Yongtai Zhuo , Yifan Zhang , Li Wang , Ya Su , Yue Yuan , Jiang Liu , Xueqian Kong , Cheng Jin View a PDF of the paper titled Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra, by Bingsen Xue and 12 other authors View PDF Abstract: Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge. Despite decades of computational efforts, no existing system achieved reliable reasoning over unseen spectra. Here, we propose MACROS, a multi-agent system automating structure elucidation by emulating expert iterative hypothesis-testing. Trained on 100M simulated and 1.6M experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques. It achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental chemical principles rather than memorizing database patterns. MACROS augments chemists via collaboration to deliver sixfold faster, 40% more accurate elucidation. MACROS establishes a scalable foundation for fully automated structure elucidation, and catalyzes accelerated molecular discovery toward autonomous laboratories. Subjects: Chemical Physics (physics.chem-ph) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14720 [physics.chem-ph] (or arXiv:2608.14720v1 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2608.14720 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bingsen Xue [ view email ] [v1] Wed, 12 Aug 2026 05:48:29 UTC (1,738 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra, by Bingsen Xue and 12 other authors View PDF view license Current browse context: physics.chem-ph < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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