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Cloudra: Geometric Reasoning and Scientific Validation for Autonomous Reaction Mechanism Exploration — arXiv:2611.02909

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02909
License (as archived): CC0-1.0
Archived: 2026-11-03T10:29:37.557Z
SHA-256 of the archived text: e7dbec819361eb0d3a768f4de2233456942aabbd0b246ba97aade682c9a1d183
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How to cite

Cloudra: Geometric Reasoning and Scientific Validation for Autonomous Reaction Mechanism Exploration — arXiv:2611.02909 — arXiv (en), https://arxiv.org/abs/2611.02909 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df61d43d4e33 (Metraipolis archive dc_df61d43d4e33, sha256 e7dbec819361eb0d3a768f4de2233456942aabbd0b246ba97aade682c9a1d183)

BibTeX

@misc{dcdf61d43d4e33,
  title = {Cloudra: Geometric Reasoning and Scientific Validation for Autonomous Reaction Mechanism Exploration — arXiv:2611.02909},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02909},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df61d43d4e33; sha256 e7dbec819361eb0d3a768f4de2233456942aabbd0b246ba97aade682c9a1d183},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02909. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous reaction-mechanism exploration requires proposing reaction steps, preparing three-dimensional structures, and determining which reactant and product a computed saddle point connects. Large language model (LLM) agents can misjudge spatial relationships and confuse a computed structure with the species or step they intended to study. We present Cloudra, a multi-agent framework that constrains mechanistic output through a Delivery DAG of chemical objects. Species nodes carry geometries and bonding topology; elementary-step edges carry transition states and reaction connections between species. Cheminformatics algorithms establish molecular identities and atom correspondences. Deterministic routines measure and transform structures to prepare candidate geometries. Frequency and IRC calculations determine the stationary points found and the species they connect. Agents use these objects to test proposed steps, compare pathways, and revise the mechanism. CloudraBench evaluates the framework's end-to-end mechanism-calculation capability across 50 tasks covering elementary reactions, multistep mechanisms, stereochemical competition, and computational method limitations. Across 200 runs under four workflow configurations, the primary Guided workflow achieves a mean composite score of 0.805, compared with 0.584 for the same-model Standard baseline. On Hard-tier tasks, the Guided mean increases from 0.689 under Standard to 0.958. Under Guided, the self-consistency component reaches its maximum score in 24 of the 40 non-Impossible tasks, compared with seven under Standard. Behavioral analyses examine how the agents use bond and dihedral measurements, optimized structures, and IRC endpoints to revise local mechanistic assignments.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2611.02909 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:33.392Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).