https://github.com/OpenDCAI/DataFlow or https://github.com/OpenDCAI/DataFlow-MM

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arXivLens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/research-on-world-models-is-not-merely-injecting-world-knowledge-into-specific-tasks-593-0cceebe3

Executive Summary
Detailed Breakdown
Practical Applications

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Papers

arxiv:2602.01630

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

Published on Feb 2

· Submitted by

bohan zeng on Feb 4

Kling Team

Upvote

Authors:

Bohan Zeng ,

Kaixin Zhu ,

Yuran Wang ,

Yifan Dai ,

Hongcheng Gao ,

Yang Shi ,

Abstract

Current world models lack unified frameworks despite task-specific advances, necessitating a comprehensive approach integrating interaction, perception, symbolic reasoning, and spatial representation.

AI-generated summary

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.