AI 中文总结
研究针对多模态大语言模型科学推理难题，提出TopoAgent自进化拓扑框架，用图进化取代线性轨迹，通过前端分解器、DAG组织原子及自适应原子裂变实现，实验证明其显著优于线性智能体框架，为科学推理提供新范式。
AI 中文摘要
虽然多模态大语言模型（MLLMs）在一般任务中表现出色，但由于整体线性规划的局限性，严格的科学推理仍然具有挑战性。这种顺序设计往往存在视觉语义错位、长上下文幻觉以及在固定任务粒度下执行脆弱等问题。我们提出了TopoAgent，这是一个自进化拓扑框架，它用动态的、状态隔离的图进化取代线性轨迹。TopoAgent首先使用前端分解器将复杂查询分解为基于视觉的原子。这些原子根据它们的依赖关系组织成有向无环图（DAG），实现严格的上下文隔离，使推理引擎免受无关历史噪声的影响。此外，我们引入了自适应原子裂变，当工具能力边界被超时时，它会在运行时将瓶颈节点动态拆分为更细粒度的子原子。在数学、物理和化学基准上的广泛实验表明，TopoAgent显著优于当前的线性智能体框架，为自主科学推理提供了一个强大、抗噪声和自我纠正的范例。
英文摘要
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.
