ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 24 of its 34 references that are in the map. First posted on arXiv 10 Oct 2026.
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24 of 34 references.
References · 24
- 2025PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understandingcited by 160
- 2024Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineeringcited by 152
- 2026DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compressioncited by 76
- 2025PAI-Bench: A Comprehensive Benchmark For Physical AIcited by 34
- 2025ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agentscited by 26
- 2026Code2Worlds: Empowering Coding LLMs for 4D World Generationcited by 19
- 2026SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learningcited by 17
- 2026BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CADcited by 15
- 20263DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Codecited by 15
- 2026Code World Model: Coding Agent as World Braincited by 14
- 2026Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoningcited by 14
- 2026PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinementcited by 10
12 more
- 2024MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?cited by 9
- 2025Widget2Code: From Visual Widgets to UI Code via Multimodal LLMscited by 9
- 2026VisRefiner: Learning from Visual Differences for Screenshot-to-Code Generationcited by 9
- 2026WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesiscited by 9
- 2026WorldClaw: Agentic 3D Open-World Generation at Scalecited by 8
- 2026VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstructioncited by 7
- 2026MPMWorlds: Material-Point-Method Simulations for Inferring and Extrapolating Physical Dynamicscited by 5
- 2026PhysMind: From Video to Executable Worlds for Training-Free Physical Reasoningcited by 4
- 2026Nova3D: Code-Native Generation of Programmable 3D Assetscited by 2
- 2026PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environmentscited by 2
- 2026From Visual Widgets to UI Code: Efficient Tool-Grounded Generationcited by 1
- 2026PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loopcited by 1
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- 2026Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation
- 2026WorldAgent: Verification-Guided Agentic Physical World Construction
- 2026VoxelCodeBench: Benchmarking 3D World Modeling Through Code Generation
- 2026Code Plans, Diffusion Renders: Open-Ended Generative World Modeling
- 2026Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos
- 2025WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
- 2026WorldBench: Evaluating LLMs on Three.js Voxel World Generation
- 2026HarnessEval-W: Agentifying the Evaluation of Visual Worlds