LiveEvalBench: Toward Open-World Evaluation for Web Generation
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Abstract (arXiv)
Large language models are increasingly capable of synthesizing executable frontend projects, yet existing benchmarks still treat web generation as a static evaluation problem. We argue that frontend artifacts demand a different paradigm: they are interactive rather than static, admit diverse yet equally valid implementations, and evolve faster than rigid pipelines can accommodate. To address these gaps, we present LiveEvalBench, an automated framework that reformulates web-generation evaluation as an agentic, adaptive, and extensible process. LiveEvalBench instantiates evaluation as a collaborative review workflow, in which a Build Engineer, a Code Engineer, and a UI Tester collectively gather evidence across the full lifecycle of a frontend project, from deployment and code inspection to browser-based interaction. To handle implementation diversity, an adaptive protocol couples shared rubrics for cross-model comparability with implementation-grounded criteria tailored to each artifact. The framework further supports incremental integration of new evaluator roles and assessment dimensions without pipeline redesign. Experiments across diverse real-world web-generation scenarios show that LiveEvalBench aligns closely with human expert judgment and provides fine-grained insights into frontier models' web generation capabilities. Code is available at https://github.com/wyysteelhead/LiveEvalBench
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 4 Aug 2026.
Builds on
21 of 41 references.
References · 21
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
- 2021Measuring Coding Challenge Competence With APPScited by 879
- 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
- 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 558
- 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 527
- 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 431
- 2024Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Modelscited by 301
9 more
- 2024Agent-as-a-Judge: Evaluate Agents with Agentscited by 223
- 2024Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineeringcited by 146
- 2017pix2code: Generating Code from a Graphical User Interface Screenshotcited by 113
- 2024Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototypingcited by 50
- 2025Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworkscited by 36
- 2025ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluationcited by 35
- 2025FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluationcited by 17
- 2025FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedbackcited by 14
- 2026WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Modelscited by 6
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Citing papers per year: 2026: 1.
1 paper.
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- 2026WebWorld: The Browser as a World Model for Self-Improving Web Code
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- 2026WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
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- 2026Framework and Benchmark for Code-Driven Agentic Testing in Web Development