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FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D — arXiv:2610.41162

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41162
License (as archived): CC0-1.0
Archived: 2026-11-02T06:58:59.681Z
SHA-256 of the archived text: c8b15ebf517365f8b8ce2ff925e7207066d91874a6f5f439ff2b665f9aad1b1b
Stable URL of this page: https://metraipolis.com/documento/dc_bf691d54dcbc

How to cite

FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D — arXiv:2610.41162 — arXiv (en), https://arxiv.org/abs/2610.41162 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf691d54dcbc (Metraipolis archive dc_bf691d54dcbc, sha256 c8b15ebf517365f8b8ce2ff925e7207066d91874a6f5f439ff2b665f9aad1b1b)

BibTeX

@misc{dcbf691d54dcbc,
  title = {FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D — arXiv:2610.41162},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41162},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf691d54dcbc; sha256 c8b15ebf517365f8b8ce2ff925e7207066d91874a6f5f439ff2b665f9aad1b1b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41162. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

To accelerate materials discovery, language models must be able to address problems at the research frontier. Yet textbook- and examination-derived benchmarks primarily assess established knowledge, leaving performance on frontier research problems largely untested. We introduce FrontierMat, a benchmark of 330 problems drawn from frontier academic and industrial research in fields including aerospace, semiconductors, embodied intelligence and nuclear fusion. Every problem is validated by leading experimentalists and industry experts in its field. Each problem presents a real study and asks the model to infer a withheld outcome or draw a conclusion from selected experimental evidence. Problems are annotated by functional domain, material system and the scale of the governing mechanism; 130 require cross-scale reasoning. The strongest general-purpose model scores only 53.7%, and on matched problems, cross-scale problems score 8.7 points below single-scale ones. Half of the problems are solved by at most one model, and models fail chiefly by endorsing what is plausible rather than what was observed. A domain-adaptation study with meta-weighted continued pretraining and scale-modular self-distillation raises an open-weight model from 36.7% to 55.4%, matching the strongest proprietary model, with improvements on public materials benchmarks and no decline in aggregate performance on general benchmarks. FrontierMat provides a testbed for measuring progress toward language models that support frontier materials R&D.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41162 — 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 2026-11-02T06:56:58.275Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).