# MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants

arXiv: https://arxiv.org/abs/2603.09652 · HTML: https://mlsanity.com/paper/2603.09652/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 88 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 30 of 31)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025)
- [MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention](https://mlsanity.com/paper/2506.13585/) (2025)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [MiMo-V2-Flash Technical Report](https://mlsanity.com/paper/2601.02780/) (2026)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models](https://mlsanity.com/paper/2503.23350/) (2025)
- [Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs](https://mlsanity.com/paper/2406.20098/) (2024)
- [WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch](https://mlsanity.com/paper/2505.03733/) (2025)
- [ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation](https://mlsanity.com/paper/2405.14125/) (2024)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation](https://mlsanity.com/paper/2507.04952/) (2025)
- [Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks](https://mlsanity.com/paper/2505.07473/) (2025)
- [Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping](https://mlsanity.com/paper/2410.16232/) (2024)
- [Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought](https://mlsanity.com/paper/2505.15431/) (2025)
- [FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow](https://mlsanity.com/paper/2505.17399/) (2025)
- [Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review](https://mlsanity.com/paper/2406.12655/) (2024)
- [FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback](https://mlsanity.com/paper/2601.04203/) (2025)
- [SceneGenAgent: Precise Industrial Scene Generation with Coding Agent](https://mlsanity.com/paper/2410.21909/) (2024)

## Built on by (most cited first, 8 of 8)

- [Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation](https://mlsanity.com/paper/2605.30000/) (2026)
- [Rubric-to-Code Credit Assignment for Reinforcement Learning](https://mlsanity.com/paper/2608.27906/) (2026)
- [EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants](https://mlsanity.com/paper/2608.29387/) (2026)
- [DashArena: Benchmarking LLMs on Interactive Analytic Dashboard Generation](https://mlsanity.com/paper/2608.10567/) (2026)
- [WebWorld: The Browser as a World Model for Self-Improving Web Code](https://mlsanity.com/paper/2608.30530/) (2026)
- [ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDi](https://mlsanity.com/paper/2609.09685/) (2026)
- [WebCraftBench: Evaluating Web Application Generation from a Software Testing Perspective](https://mlsanity.com/paper/2609.15387/) (2026)
- [Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development](https://mlsanity.com/paper/2610.13022/) (2026)

## Abstract (arXiv)

With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applications, which we term MiniApps. These applications require models to not only render visual interfaces but also construct customized interaction logic that adheres to real-world principles. However, existing benchmarks primarily focus on algorithmic correctness or static layout reconstruction, failing to capture the capabilities required for this new paradigm. To address this gap, we introduce MiniAppBench, the first comprehensive benchmark designed to evaluate principle-driven, interactive application generation. Sourced from a real-world application with 10M+ generations, MiniAppBench distills 500 tasks across six domains (e.g., Games, Science, and Tools). Furthermore, to tackle the challenge of evaluating open-ended interactions where no single ground truth exists, we propose MiniAppEval, an agentic evaluation framework. Leveraging browser automation, it performs human-like exploratory testing to systematically assess applications across three dimensions: Intention, Static, and Dynamic. Our experiments reveal that current LLMs still face significant challenges in generating high-quality MiniApps, while MiniAppEval demonstrates high alignment with human judgment, establishing a reliable standard for future research. Our homepage is available in miniappbench.github.io.