arXiv Computer Vision By Hanyang Wang, Yimo Cai, Weiliang Chen, Jiawei Chi, Haowen Sun, Qiyu Dai, Yi-Hsin Hung, Xingzhuo Guo, Jinshan Ren, Runmao Yao, Ziwei Liu, Mingsheng Long, Yueqi Duan, Jun Gao, Jiangran Lyu, Fangfu Liu, Jialong Wu

Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning

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arXiv AI
3d ago

CoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness

CoWorld Reasoner is an agentic framework that leverages a Video World Model (VWM) as a unified visual reasoning engine for Vision‑Language Models (VLMs). It exposes multiple visual operations through tools and skills, allowing the VLM to decide when to imagine, select an operation, generate visual content, and interpret the resulting evidence. The framework includes a VLM‑VWM Harness Co‑Evolution mechanism that jointly refines tools, skills, and execution code based on feedback, while keeping model weights fixed. Across seven benchmarks covering spatial, temporal, causal, and perceptual reasoning, CoWorld Reasoner outperforms direct VLM reasoning and improves accuracy on all benchmarks, achieving notable gains on MMSI and ERQA.

By Dongjie Cheng, Ruifeng Yuan, Ran Wei, Ning Song, Yongqi Li, Wenjie Li
arXiv Computer Vision
Oct 2

PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop

PhysVista is a new benchmark that evaluates physical intelligence in Vision‑Language Models (VLMs) by integrating perception, reasoning, and plausibility assessment into a closed cognitive loop. It distinguishes between event‑level and scale‑level reasoning and tests models on both real‑world and AI‑generated videos to provide a holistic, fine‑grained analysis of physical understanding. Experiments show significant gaps in VLMs’ physical reasoning and plausibility assessment, underscoring the need for more principled, physically grounded multimodal designs.

By Xinge Peng, Yiting Lu, Tianwu Zhi, Wen Wen, Jianzhao Liu, Xin Li, Zhibo Chen
arXiv Computation and Language
Aug 27

Code World Model: Coding Agent as World Brain

The paper introduces Code World Model, a framework that decouples world evolution from visual rendering by using a coding agent as a world brain. The agent reasons about events, generates executable code to maintain persistent state, and a proxy representation links this state to a video model for high‑fidelity visual output. Experiments with MiniMax‑H3 show that the system can follow proxy‑based spatiotemporal specifications while preserving rich visual dynamics, illustrating a new approach to open‑ended world modeling.

By Yiwen Chen, Guosheng Lin, Chi Zhang