Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

TL;DR

This position paper argues that Multimodal Large Language Models (MLLMs) can significantly advance scientific reasoning across mathematics, physics, chemistry, and biology by integrating diverse data modalities and reasoning strategies. It introduces a four‑stage roadmap toward Artificial General Intelligence (AGI), analyzes data heterogeneity across domains, and identifies five core reasoning paradigms that enable cross‑domain problem solving. The authors discuss eight future directions, emphasize the need for unified, explainable, and collaborative MLLMs, and acknowledge alternative views while proposing practical mitigations for challenges such as data diversity and hallucinations. Overall, the work provides a strategic vision and actionable directions for advancing MLLMs in scientific reasoning with broad potential impact on research, education, and discovery.

Abstract

Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still struggle with generalization across domains and often fall short of multimodal perception. Multimodal Large Language Models (MLLMs), which integrate text, images, and other modalities, present an exciting opportunity to overcome these limitations and enhance scientific reasoning. Therefore, this position paper argues that MLLMs can significantly advance scientific reasoning across disciplines such as mathematics, physics, chemistry, and biology. First, we propose a four-stage research roadmap of scientific reasoning capabilities, and highlight the current state of MLLM applications in scientific reasoning, noting their ability to integrate and reason over diverse data types. Second, we summarize the key challenges that remain obstacles to achieving MLLM's full potential. To address these challenges, we propose actionable insights and suggestions for the future. Overall, our work offers a novel perspective on MLLM integration with scientific reasoning, providing the LLM community with a valuable vision for achieving Artificial General Intelligence (AGI).

Figures (5)

• Figure 1: The big picture of our position. We focus on multimodal scientific fields, especially mathematics, physics, chemistry, and biology as our scope (a), and we advocate leveraging MLLMs with multiple reasoning functions for scientific reasoning (b). We further propose a four-stage roadmap for scientific reasoning capability, ultimately achieving AGI (c).
• Figure 2: Overview of MLLM-based scientific reasoning paradigms and corresponding reasoning capabilities.
• Figure 3: Challenges for MLLM-based scientific reasoning.
• Figure 4: Eight prospects for the future of MLLMs in the field of multimodal scientific reasoning. We base our core expectation on developing unified scientific MLLMs, and further elaborate our prospects from four high-level aspects: input side, output side, environments interact with itself, and internal reasoning schemes.
• Figure 5: Illustrations of alternative view 1 (a) and view 2 (b), as well as our corresponding counterarguments.