Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

2025-02-05

AI Summary

Current scientific reasoning models face significant bottlenecks in cross-disciplinary generalization and multimodal perception, limiting their deep application across mathematics, physics, chemistry, and biology. To address this, we propose the first capability evolution framework for multimodal large language models (MLLMs) tailored to scientific reasoning, systematically organizing development into four stages: representation, alignment, injection, and reasoning. Leveraging multimodal representation learning, cross-modal alignment, scientific knowledge injection, and chain-of-reasoning enhancement, our approach substantially improves logical deduction, evidence integration, and cross-domain generalization. This work establishes MLLMs as a foundational paradigm for scientific reasoning—identifying and overcoming critical technical barriers. It delivers a theoretically grounded yet practically viable framework and actionable roadmap for deploying artificial general intelligence (AGI) in authentic scientific domains.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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).

Problem

• MLLMs enhance scientific reasoning across multiple disciplines.
• Addressing generalization and multimodal perception challenges in scientific models.
• Proposing a roadmap for MLLM integration with scientific reasoning.

Innovation

• Multimodal Large Language Models
• Integration of text and images
• Four-stage research roadmap