EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants

arXiv:2608.29387 · cs.AI, cs.SE · Submitted 2026-08-29 · Read on arXiv

cs.AI, cs.SE

Submitted: 2026-08-29

Updated: 2026-09-07

Comments: Accepted to the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)

Code: https://github.com/MAPS-research/EvoGenUI-Bench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Actor — 33× in this paper · explained in 3 episodes
• Benchmark — 27× in this paper · explained in 2 episodes
• Trace — 21× in this paper · explained in 2 episodes
• Turn Pass — 10× in this paper
• Alignment — 9× in this paper · explained in 1 episode
• Grounding — 9× in this paper · explained in 3 episodes
• Affordance — 7× in this paper · explained in 1 episode
• Benchmarking — 7× in this paper · explained in 3 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Adjacent Pass Retention — 2× in this paper
• Consecutive Passed Turns — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Failure Taxonomy — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Alignment Scores — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes

Sources

• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
• Generative UI: LLMs are Effective UI Generators
• SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks
• FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow
• ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
• FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

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