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MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
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“Metrics for error recovery remain underdeveloped, and systems often struggle with conceptual mistakes, cascading faults, or complex edge cases [105,136,165].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [165]
In the citing paper
“Metrics for error recovery remain underdeveloped, and systems often struggle with conceptual mistakes, cascading faults, or complex edge cases [105,136,165].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [165]
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Metadata
- Authors
- Ni, B., & Buehler, M. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Buehler, Markus J.
- Venue
- Extreme Mechanics Letters
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 67
- Pages
- 102131
- ISSN
- 2352-4316 (ISSN-L)
- OpenAlex
- W4391862446
- Cited by
- 113
- References
- 60
- Citations by year
- 2026: 59 · 2025: 40 · 2024: 13
- FWCI
- 14.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 350 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Extreme Mechanics Letters (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- USDA
- NIH
- Army Research Laboratory
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- multi-agent systems, large language models, mechanics problem solving, elasticity problems, finite element analysis, code generation, self-correction, physics-informed machine learning, knowledge integration, hyperelasticity, engineering automation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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