世界模型研究並非僅是將世界知識注入特定任務

摘要

世界模型已成為人工智慧研究的關鍵前沿，其目標是通過注入物理動態與世界知識來增強大型模型。核心目標在於使智能體能夠理解、預測並與複雜環境互動。然而當前研究格局仍呈現碎片化，主流方法多側重於將世界知識注入孤立任務（如視覺預測、三維估計或符號接地），而非建立統一的定義或框架。儘管這類任務特定整合能提升性能，卻往往缺乏整體世界理解所需的系統性協調。本文剖析了碎片化方法的局限性，提出世界模型的統一設計規範。我們主張，健全的世界模型不應是能力的鬆散集合，而應成為融合互動、感知、符號推理與空間表徵的規範性框架。此研究旨在提供結構化視角，引導未來研究朝向更具通用性、穩健性及原則性的世界模型發展。

English

World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world.

世界模型研究並非僅是將世界知識注入特定任務

Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks

摘要

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