异构科学基础模型协同

Heterogeneous Scientific Foundation Model Collaboration

April 30, 2026

作者: Zihao Li, Jiaru Zou, Feihao Fang, Xuying Ning, Mengting Ai, Tianxin Wei, Sirui Chen, Xiyuan Yang, Jingrui He

cs.AI

摘要

智能体化大型语言模型系统已展现出强大能力。然而其以语言作为通用接口的依赖，从根本上限制了其在许多现实问题中的适用性，特别是在科学领域——该领域已开发出针对自然语言之外专业任务的领域专用基础模型。本研究提出Eywa异构智能体框架，旨在将语言中心系统扩展至更广泛的科学基础模型类别。Eywa的核心思想是通过基于语言模型的推理接口增强领域专用基础模型，使语言模型能够指导非语言数据模态的推理。该设计让通常针对专业数据和任务优化的预测性基础模型，得以参与智能体系统内更高层次的推理与决策过程。Eywa既可作为单智能体流程的即插即用替代方案（EywaAgent），也可通过将传统智能体替换为专用智能体（EywaMAS）集成至现有多智能体系统。我们进一步研究基于规划的编排框架，其中规划器动态协调传统智能体与Eywa智能体，以解决跨异构数据模态的复杂任务（EywaOrchestra）。我们在涵盖物理、生命与社会科学的多领域实验中评估Eywa，结果表明：通过与专用基础模型的有效协作，Eywa在涉及结构化与领域专用数据的任务上提升性能，同时降低对基于语言推理的依赖。

English

Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability to many real-world problems, especially in scientific domains where domain-specific foundation models have been developed to address specialized tasks beyond natural language. In this work, we introduce Eywa, a heterogeneous agentic framework designed to extend language-centric systems to a broader class of scientific foundation models. The key idea of Eywa is to augment domain-specific foundation models with a language-model-based reasoning interface, enabling language models to guide inference over non-linguistic data modalities. This design allows predictive foundation models, which are typically optimized for specialized data and tasks, to participate in higher-level reasoning and decision-making processes within agentic systems. Eywa can serve as a drop-in replacement for a single-agent pipeline (EywaAgent) or be integrated into existing multi-agent systems by replacing traditional agents with specialized agents (EywaMAS). We further investigate a planning-based orchestration framework in which a planner dynamically coordinates traditional agents and Eywa agents to solve complex tasks across heterogeneous data modalities (EywaOrchestra). We evaluate Eywa across a diverse set of scientific domains spanning physical, life, and social sciences. Experimental results demonstrate that Eywa improves performance on tasks involving structured and domain-specific data, while reducing reliance on language-based reasoning through effective collaboration with specialized foundation models.