ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
cs.CV
Submitted: 2026-10-10
Updated: 2026-10-10
Code: https://github.com/anthropics/claude-code
Project page: https://helen1p.github.io/ExeWorldBench/ABSTRACT
License: http://creativecommons.org/licenses/by/4.0/
The gist: Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works.
Terminology
Abstract
Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works. This shift calls for executable world modeling, which integrates two core capabilities: world understanding, reasoning about the underlying structural, mechanical, and causal principles of a world, and executable realization, translating that understanding into a code-based system whose behavior emerges from physical interactions. However, existing benchmarks provide only fragmented evaluations centered on physical reasoning, visual fidelity, or behavioral correctness, leaving the integration of these capabilities insufficiently assessed. To bridge this gap, we introduce ExeWorldBench, to our knowledge the first benchmark to comprehensively evaluate world understanding and executable realization through code-based 3D world construction. ExeWorldBench comprises three progressively challenging tasks: Mechanism Executability, which evaluates the realization of individual mechanisms from visual observations; Compositional Executability, which extends to multi-stage systems with spatial, mechanical, and causal dependencies; and Intent-Driven Executable World Synthesis, which requires constructing executable worlds from high-level goals and physical requirements. Extensive experiments on frontier models reveal substantial limitations in executable world modeling, offering insights into the development of more physically grounded and causally coherent world construction systems.
Sources
- Code World Model: Coding Agent as World Brain
- VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generation
- 3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
- WorldClaw: Agentic 3D Open-World Generation at Scale
- ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents
- SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
- MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamics
- VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction
- WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis
- Nova3D: Code-Native Generation of Programmable 3D Assets
- PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop
- Coding Agent Is Good As World Simulator
- Training and Benchmarking Code Generation for Physics-Inspired Animations
- PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
- PhysMind: From Video to Executable Worlds for Training-Free Physical Reasoning
- BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD
- From Visual Widgets to UI Code: Efficient Tool-Grounded Generation
- Code2Worlds: Empowering Coding LLMs for 4D World Generation
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