Evaluation of 19 multimodal large language models shows that current MLLMs still cannot reliably make justified and evidence-bounded inferences from experimental results, which is an essential capability in real scientific discovery.
Abstract
Large language models (LLMs) are increasingly involved in scientific discovery, yet it remains unclear whether they can support complex real laboratory science. Here we introduce Science Edge Evaluation (SEE), a multimodal benchmark of expert-curated questions grounded in peer-reviewed literature and experimental practice in chemistry, biology, and materials science. Evaluation of 19 multimodal large language models (MLLMs) shows that even the best-performing model reaches only 48.7% accuracy. Moreover, general-purpose models outperform science-specialized models on average. In the visual-agent evaluation, the use of tools increases the best accuracy to 52.7%. Tool use can expand the information available to models, but more information does not necessarily lead to reliable scientific reasoning. The key challenge is whether models can manage tool-derived information within the boundaries of the original experimental evidence. Together, these findings reveal that current MLLMs still cannot reliably make justified and evidence-bounded inferences from experimental results, which is an essential capability in real scientific discovery. Bridging this gap requires MLLMs to transition from explaining established scientific concepts to deriving novel and evidence-based insights from experimental data.
Onepot-Bench 0 is introduced, a proprietary benchmark suite for evaluating language models on synthetic chemistry capabilities relevant to wet-lab execution and probes basic competency, reliability, and deeper knowledge, all skills which are required for reliable performance in the lab.
Brandon Wang, Andrei S. Tyrin, Daniil A. Boiko· 0 citations
Scientific equation discovery has long been central to scientific progress, proceeding through iterative cycles of hypothesis generation, observational testing, and refinement under scientific constraints. As LLM capabilities advance and their role in AI for Science expands, it remains an open problem whether they can genuinely discover scientific laws and how this ability should be evaluated. Existing evaluations, however, often either simplify discovery through synthetic settings or reuse published targets that may already be familiar to LLMs. We therefore introduce SCILAWS-BENCH, a benchmark for scientific law discovery built from published research and real scientific data. It comprises 118 problems drawn from 381 scientific papers, covering 291 candidate laws and roughly 8M real data points across six scientific disciplines. Each problem is instantiated in two complementary settings: (1) SCILAWS-REAL asks models to propose laws from fixed real observations and evaluates held-out predictive fit and scientific validity derived from the source literature, and (2) SCILAWS-PARALLEL asks models to actively query residual-calibrated worlds and recover synthesized hidden laws derived from published forms. This two-setting task design preserves each problem's scientific context while separately evaluating fixed-record law discovery and active recovery of a newly synthesized hidden law. We find that predictive fit can diverge from scientific validity, memorization shapes whether models reproduce or move beyond published formulas, and our best-of-N study reveals a selection bottleneck. Our work provides a paper-grounded benchmark and new empirical perspectives for evaluating AI for scientific discovery. Project page: https://yiyihum.github.io/SciLaws-Bench
Yi-Ming Huang, Zi-Che Liu, Zhuo-Hang Wu et al.· 0 citations
Existing benchmarks for scientific data analysis evaluate LLMs primarily on code execution or workflow completion, overlooking that scientific analysis serves to support distinct types of scientific claims: hypothesis exploration, statistical inference, mechanistic explanation, each with different assumptions and validity criteria. We introduce SDABench, a benchmark that reorganizes evaluation around six capabilities (descriptive, exploratory, inferential, predictive, causal, and mechanistic) across five domains (Biology, Chemistry, Environment, Geography, Physics). SDABench comprises 527 real-data instances (SDA-Real) and 6000 synthetic instances (SDA-Synth), each in both multiple-choice and open-ended formats, constructed through an automated pipeline. Evaluating 15 representative LLMs, we find that models handle descriptive analysis well but degrade sharply on tasks requiring assumption selection, latent-process modeling, or mechanistic reasoning. SDABench further provides a five-stage error analysis framework that locates where LLMs fail: more advanced models more reliably identify the relevant scope and variables, but still struggle to select appropriate analytical procedures, model variable relationships, and draw valid conclusions.
Chuhan Shi, Xiaoquan Ren, Sicheng Song et al.· 1 citation
High-quality biological databases are the bedrock of data-driven scientific discovery. However, the construction of these resources remains a labor-intensive bottleneck, particularly for emerging research frontiers where structured data is non-existent. While LLM-based agents have catalyzed progress in downstream scientific modeling, their potential to automate the critical upstream challenge of database curation remains largely untapped. To bridge this gap, we introduce BioDataLab, a rigorous benchmark comprising 100 tasks meticulously derived from 57 high-impact database publications. BioDataLab evaluates the capability of autonomous agents to transform raw, heterogeneous biological resources into structured, analysis-ready databases. Unlike static evaluations, BioDataLab provides a fully interactive environment encompassing data retrieval, extraction, annotation, and integration, featuring process-oriented curation targets and contamination-control checks. We benchmark 11 state-of-the-art LLMs (including Gemini-3.0, GPT-5.2, and Claude-4.5) under different agent frameworks, revealing a substantial capability gap: the top-performing model achieves only a 40% success rate. Further error analysis identifies significant bottlenecks in multi-step tool orchestration and adherence to complex biological data formats. These findings underscore that while LLMs are proficient in downstream reasoning, autonomous upstream curation remains a formidable frontier. All data and codes are available at GitHub.
Jiaxian Yan, Xi Fang, Jintao Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course toward artificial general intelligence and emerging as a central issue in the global technological race. This article conducts a comprehensive review of the core technologies that support LLMs from a user’s standpoint, including prompt engineering, knowledge-enhanced retrieval-augmented generation (RAG), fine-tuning, pre-training, and tool learning. In addition, it traces the historical development of Science of Science (SciSci) and presents a forward-looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent-based model for scientific evaluation and presents new research fronts in detection and knowledge graph building methods with LLMs.
Guoqiang Liang, Jingqian Gong, Mengxuan Li et al.· Journal of information scien...· 0 citations
The Structural Gap Hypothesis Agent (SGHA), a fully automated, corpus-first research-problem discovery system that runs entirely on a local LLM, is introduced and compared with the AI Scientist-v2 idea formulation module in five machine-learning domains.