異質性科學基礎模型協作

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

基於代理的大型語言模型系統已展現出強大能力。然而，其依賴語言作為通用介面的特性，從根本上限制了其在許多現實問題中的適用性，特別是在科學領域——這些領域已開發出針對特定領域的基礎模型，以處理自然語言之外的專業任務。本研究提出Eywa，一個異構代理框架，旨在將以語言為核心的系統擴展至更廣泛的科學基礎模型類別。Eywa的核心思想是為領域專用基礎模型配備基於語言模型的推理介面，使語言模型能夠引導對非語言數據模態的推斷。此設計讓通常針對專用數據和任務優化的預測性基礎模型，能夠參與代理系統中的高層次推理與決策過程。Eywa可作為單一代理流程的無縫替代方案（EywaAgent），亦可通過以專業代理取代傳統代理的方式整合至現有多代理系統中（EywaMAS）。我們進一步研究基於規劃的協調框架，其中規劃器動態協調傳統代理與Eywa代理，以解決跨異構數據模態的複雜任務（EywaOrchestra）。我們在多個科學領域（涵蓋物理、生命與社會科學）中對Eywa進行評估。實驗結果表明，Eywa在涉及結構化與領域專用數據的任務上提升了性能，同時通過與專業基礎模型的有效協作，降低了對基於語言的推理的依賴。

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.