TopoAgent: A Self-Evolving Topological Agent for Multimodal Scientific Reasoning
Mingze Xu, Yinghui Li, Jiayi Kuang, Zhanhui Kang, Di Yin, Ying Shen, Xing Sun, Yuxing Han
Why It Matters
What makes this one worth your time
This research addresses significant limitations in current multimodal large language models, potentially improving their applicability in rigorous scientific contexts.
TopoAgent revolutionizes scientific reasoning by employing dynamic graph evolution for enhanced multimodal processing.
Summary
The paper introduces TopoAgent, a self-evolving topological framework that enhances multimodal scientific reasoning by replacing linear planning with dynamic graph evolution, addressing issues like visual-semantic misalignment and context hallucinations.
Key contributions
- Introduction of a self-evolving topological framework for scientific reasoning.
- Development of a front-end decomposer to fracture complex queries into visually-grounded atoms.
- Implementation of adaptive atomic fission to dynamically manage reasoning complexity.
Notable insights
- The use of a Directed Acyclic Graph (DAG) for context isolation is a novel approach to mitigate historical noise in reasoning tasks.
- Adaptive atomic fission allows for real-time adjustments to reasoning complexity, which could enhance performance in dynamic environments.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.14658v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel in general tasks, rigorous scientific reasoning remains challenging due to the limitations of monolithic, linear planning. Such sequential designs often suffer from visual-semantic misalignment, long-context hallucinations, and brittle execution under fixed task granularity. We propose TopoAgent, a self-evolving topological framework that replaces linear trajectories with dynamic, state-isolated graph evolution. TopoAgent first employs a front-end decomposer to fracture complex queries into visually-grounded atoms. These atoms are organized into a Directed Acyclic Graph (DAG) based on their dependencies, enabling strict context isolation to shield the reasoning engine from irrelevant historical noise. Furthermore, we introduce adaptive atomic fission, which dynamically splits bottleneck nodes into finer-grained sub-atoms at runtime when tool capability boundaries are exceeded. Extensive experiments across mathematics, physics, and chemistry benchmarks demonstrate that TopoAgent significantly outperforms state-of-the-art linear agent frameworks, providing a robust, noise-resistant, and self-correcting paradigm for autonomous scientific reasoning.