WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis
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Abstract (arXiv)
Large language models (LLMs) are increasingly asked not only to write static interfaces, but to construct executable interactive worlds from natural language. Browser-native 3D, commonly built with Three.js, is a natural next frontier: generated programs must integrate assets, obey spatial and physical constraints, and keep user-facing controls synchronized with hidden runtime state. Existing web-generation benchmarks and evaluators, however, largely observe only pixels or DOM nodes, while the mechanics of a Three.js world unfold inside an opaque <canvas>. We introduce WorldCoder-Bench, a benchmark for autonomous, physically grounded 3D world synthesis. WorldCoder-Bench contains 2,026 expert-curated tasks across Simulation, Rendering, and Application scenarios, with optional .glb assets and hidden behavioral contracts. We further propose StateProbe, an execution-based protocol that probes generated programs in a sandboxed browser and verifies hidden, mutation-hardened contracts over runtime states and transitions. Beyond verification coverage, we report Return on Automation and Time Efficiency Multiplier to measure correctness-adjusted cost and time savings. Across nine frontier models, the best system reaches only 27.8% verification coverage on WorldCoder-Core and 19.9% on WorldCoder-Robust, with failures dominated by state-schema drift and broken interaction chains rather than missing scene elements. Utility metrics further show that cheap or fast models can still provide substantial value on easier domains. WorldCoder-Bench is available at https://anonymous.4open.science/r/WorldCoder-Bench/.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 1 Jun 2026.
Builds on
31 of 53 references.
References · 31
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19 more
- 2025Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generationcited by 65
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototypingcited by 50
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Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Modelscited by 1
- 2026Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspectivecited by 0
- 2026LEGO-Anything: Coding Agents for 3D Scene Reconstructioncited by 0
- 2026Code4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenescited by 0
- 2026WorldBench: Evaluating LLMs on Three.js Voxel World Generationcited by 0
- 2026MESSENGER: Memory-Enhanced Sequential Scene Flow Estimation via Autoregressive Next-Frame Forecastingcited by 0
- 2026MESSENGER: Memory-Enhanced Sequential Scene Flow Estimation via Autoregressive Next-Frame Forecasting7 Oct 2026
- 2026WorldBench: Evaluating LLMs on Three.js Voxel World Generation7 Oct 2026
- 2026Code4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenes29 Sep 2026
- 2026LEGO-Anything: Coding Agents for 3D Scene Reconstruction28 Sep 2026
- 2026Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective31 Aug 2026
- 2026Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models26 Aug 2026