Searcharxiv⌕ Search

arXiv · 2610.32575

PHYRD: A Living Benchmark for Reproducing and Extending Theoretical Physics Research

Abstract

Large language models and agents have shown significant power in advanced physical research. In this paper, We introduce PHYRD, a living benchmark for theoretical-physics reasoning with 63 Reproduce and 43 Discover problems. Reproduce tests derivation of published results; Discover tests consequences of modified theoretical conditions. FormulaForest constructs the tasks with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy ranges from 49.21% to 68.25% on Reproduce and from 18.60% to 74.42% on closed-book Discover. With tools, four models complete more Discover problems, but conceptual errors and missing arguments persist. Besides, our experimental results demonstrate the value of FormulaForest as synthetic data. On FrontierScience, a benchmark developed by OpenAI, it raises the accuracy of a Qwen-35B-A3B model from 61% to 72%.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qituan Zhang, Guo Chen, Linfeng Zhang, Wenlong Zhang. 2026-10-23. PHYRD: A Living Benchmark for Reproducing and Extending Theoretical Physics Research. https://arxiv.org/abs/2610.32575

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A survey of diversity quantification in natural language processing: The why, what, where and how

The concept of diversity received increasing attention in natural language processing (NLP) in recent years. It became an advocated property of datasets and systems, with many measures used to quantify it, but also many cross-paper inconsistencies. To address this fragmentation, we take inspiration from other scientific fields where diversity has been more thoroughly conceptualized. We build upon (Sarkar, 2010) and (Stirling, 2007), two unified cross-domain frameworks inspired from ecology, economy and other domains. The first one advocates explicit justification of diversity endorsement. The latter distinguishes three dimensions of diversity: variety, balance, and disparity. Surveying over 300 recent diversity-related papers from ACL Anthology, we propose an NLP-specific framework with 4 perspectives: why diversity is important, what diversity is measured on, where it is measured, and how. We find that in NLP richness (the simplest variety) and distance averaging (the simplest disparity) are the most prevalent measure families, and that pure balance measures are nearly absent. Moreover, half of the measures used in NLP do not conform to Stirling's framework. Our analysis increases comparability of approaches to diversity in NLP, allowing us to formulate recommendations for the field. We recommend that the field systematically use a unified terminology, and that it engage in debates about the desirable properties of diversity measures as well as about the normative assumptions behind their use.

cs.CL↗

What MLLMs Learn about When they Learn about Multimodal Reasoning

Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark of textbook-style geometry problems that exposes this assumption by operationally decomposing performance into perception, reasoning, and multimodal-specific components. Each problem is derived from a symbolic specification and accompanied by visual diagrams, text-only variants, multimodal questions, and targeted perceptual probes, enabling controlled measurement of each component. Using this decomposition, we show that common training strategies are associated with systematically different capability profiles that are invisible under aggregate accuracy. Across the models we study, reinforcement learning primarily improves perceptual grounding and robustness to diagram variation, while our analysis suggests that textual SFT yields gains through reflective reasoning. In contrast, as perception and reasoning improve, a growing fraction of remaining errors fall outside these components and are categorized as multimodal-specific. These results suggest that apparent progress in multimodal reasoning reflects shifting balances among subskills rather than uniform advancement, motivating evaluation beyond scalar accuracy.

cs.CL↗

EvalCards: A Framework for Standardized Evaluation Reporting

Evaluation reports guide the use of general-purpose AI (GPAI) models, yet inconsistent reporting often leaves essential questions unanswered. Through a review of prior work, we identify three central issues in evaluation reporting practices today, reproducibility, accessibility, and accountability, and examine how existing reporting artifacts fall short in addressing them. We introduce the EvalCards framework, outlining its key dimensions and presenting a preliminary format for concise evaluation summaries. We conclude by discussing how the framework can guide the future development of evaluation reporting standards.

cs.CL↗