# OmniMatBench: A Human-Calibrated Multimodal Reasoning Benchmark Across 19 Materials Science Subfields

arXiv: https://arxiv.org/abs/2605.29833 · HTML: https://mlsanity.com/paper/2605.29833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 28 May 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 13 of 13)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes](https://mlsanity.com/paper/2601.11659/) (2026)
- [Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale](https://mlsanity.com/paper/2603.25040/) (2026)
- [MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science](https://mlsanity.com/paper/2510.12171/) (2025)
- [Seeing Beyond Words: MatVQA for Challenging Visual-Scientific Reasoning in Materials Science](https://mlsanity.com/paper/2505.18319/) (2025)
- [MSQA: Benchmarking LLMs on Graduate-Level Materials Science Reasoning and Knowledge](https://mlsanity.com/paper/2505.23982/) (2025)
- [MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis](https://mlsanity.com/paper/2509.11335/) (2025)
- [MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science](https://mlsanity.com/paper/2602.00376/) (2026)
- [L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks](https://mlsanity.com/paper/2510.20976/) (2025)
- [PolyReal: A Benchmark for Real-World Polymer Science Workflows](https://mlsanity.com/paper/2604.02934/) (2026)

## Abstract (arXiv)

As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdisciplinary, multimodal, and application-driven nature. However, existing materials benchmarks mainly focus on property prediction, knowledge QA, or characterization understanding, leaving the broader reasoning process from materials knowledge to application underexplored. To fill this gap, we present OmniMatBench, a human-calibrated multimodal reasoning benchmark for materials science. OmniMatBench contains 3,171 expert-curated QA and calculation problems across 19 materials-science subfields, spanning fundamental materials knowledge, structural and engineering materials, materials processing and manufacturing, and functional and applied materials. We evaluate 13 open-source and closed-source MLLMs and find that the best model achieves only a 0.372 overall score, revealing a substantial gap in current materials-science reasoning. Further analysis shows strong variation across subfields, fixed reasoning heuristics, uneven materials knowledge, and limited high-level knowledge application under formula-, retrieval-, and code-assisted settings. OmniMatBench provides crucial insights into the capabilities and limitations of current MLLMs and establishes a foundation for reliable AI assistants in materials-science research.