The paper introduces a benchmark of 250 expert-curated questions on inertial confinement fusion to evaluate language models on scientific fluency, physical reasoning, and quantitative analysis. It compares various closed‑source, open‑source, and a specialized model called Prospero, showing that Prospero improves by 10.7 percentage points and that frontier models still make critical physics errors. Retrieval assistance helps smaller models but not frontier ones, and two stress tests highlight the need for expert assessment of physical assumptions.
By P. B. Radha, D. D. Meyerhofer, M. Bhattarai, B. Cheng, L. Diaz, C. L. Rousculp, A. G. Seaton, G. Gupta, P. Gurumurthy, V. Gutta, A. Jhunjhunwala, A. Vem, P. Cook, G. Rowe
arXiv:2510. 12171v2 Announce Type: replace Abstract: Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied.
By Junkai Zhang, Jingru Gan, Xiaoxuan Wang, Zian Jia, Changquan Gu, Jianpeng Chen, Yanqiao Zhu, Mingyu Derek Ma, Dawei Zhou, Ling Li, Wei Wang
The paper investigates how large language models handle domain-specific jargon, comparing a general-purpose Llama‑3.1 with a version fine‑tuned on medical data. Two new medical jargon benchmarks reveal that the general model actually outperforms the fine‑tuned variant, and interpretability tools show the fine‑tuned model over‑emphasizes a few components linked to jargon predictions. Reweighting these components narrows the performance gap, and some jargon‑sensitive components also aid materials‑science tasks, indicating a partially domain‑agnostic representation of specialized terminology.
By Darin Keng, Zhewei Sun
SCICONVBENCH is a benchmark designed to evaluate large language models (LLMs) on multi‑turn clarification tasks in computational science. It focuses on two key abilities: eliciting missing information (disambiguation) and resolving contradictory requests (inconsistency resolution) across four domains—fluid mechanics, solid mechanics, materials science, and partial differential equations. The benchmark pairs a structured task ontology with a rubric‑based evaluation framework, measuring LLM performance in clarification behavior, conversational grounding, and final‑specification fidelity, and reveals that even top models only resolve about 52.7% of disambiguation cases in fluid mechanics while often making ungrounded assumptions.
By Nithin Somasekharan, Youssef Hassan, Shiyao Lin, Gihan Panapitiya, Patrick Emami, Anurag Acharya, Sameera Horawalavithana, Shaowu Pan
arXiv:2610.34391v1 Announce Type: cross
Abstract: Scientific literature in materials physics is growing faster than researchers can manually inspect it, yet most retrieval tools rely on keyword match...
By Ivan Iorsh, Aleksandr Kapitonov, Ivan Filianin, Matvey Lesonen, Nikita Chizhov
Auto-Research Arena is a multi-target benchmark designed to evaluate autonomous research systems and uncover new ideas in language modeling. It separates admissibility—determined by model quality and resource constraints—from improvement, using nine diverse targets covering model, data, training, and inference efficiency. The benchmark, applied to language model pretraining, distinguishes existing research harnesses and reveals significant room for improvement, while also enabling the reuse of discovered ideas to achieve lower training loss and better held‑out performance.
By Xi Chen, Chongjian Ge, Yiran Xu, Yiqun Mei, Henrik I. Christensen, Hao Tan, Ziwen Chen, Hanwen Jiang