Asuka-Bench is a new benchmark of 50 web tasks with 784 criteria that evaluates 8 LLMs in 2 frameworks on multi-round refinement, finding a 38-point spread in weighted task pass rate and a top score of only 52% after three rounds.
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Webgen-bench: Evaluating llms on generating interactive and functional websites from scratch
13 Pith papers cite this work. Polarity classification is still indexing.
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2026 13representative citing papers
Cookie-Bench is a reference-free 1,000-query web development benchmark paired with Cookie-Frame, a metacognition-inspired three-stage framework (static perception, agent interaction, dynamic scoring) that aligns with human ratings on 13 frontier LLMs.
WebGameBench is a new benchmark that evaluates coding agents on building browser-native games from frozen specifications, with runtime browser evaluation showing best agents reach 76.9% usable rate but only 20.2% excellent rate.
SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
TDDev automates the full TDD loop for web app generation from requirements, delivering 34-48 percentage point quality gains and zero manual intervention in user studies.
GameGen-Verifier decomposes game specifications into keypoints, injects runtime states for targeted checks, and achieves 92.2% accuracy on 100 games while running up to 16.6x faster than agent-based baselines.
SWE-WebDevBench finds that AI app builders commonly fail at translating business needs into complete, secure, production-ready software due to specification bottlenecks, frontend-backend decoupling, low engineering quality, and security weaknesses.
DiagEval applies trajectory-conditioned diagnostic probes to recover 45.6-62.1% of misattributed failures in GUI-agent software evaluation, raising accuracy from 69.9% to 78.3% on WebDevJudge-Unit and 65.0% to 81.6% on RealDevBench.
Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
WebGen-R1 uses end-to-end RL with scaffold-driven generation and cascaded rewards for structure, function, and aesthetics to transform a 7B model into a generator of deployable multi-page websites that rivals much larger models.
MM-WebAgent is a hierarchical multimodal agent that coordinates AIGC tools through planning and iterative self-reflection to generate coherent, visually consistent webpages and outperforms baselines on a new benchmark.
A Paper-to-Interactive-System Agent and I-WebGenBench benchmark with 19 papers enable converting scientific PDFs into executable interactive web systems, with PaperVoyager framework shown to improve quality.
HTMLCure uses browser-executed interaction trajectories to diagnose and repair LLM HTML outputs, expanding 97K prompts into a 40K refined SFT set that lifts a 27B model to 50.6 on HTMLBench-400 and 81.2 on MiniAppBench.
citing papers explorer
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Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation
Cookie-Bench is a reference-free 1,000-query web development benchmark paired with Cookie-Frame, a metacognition-inspired three-stage framework (static perception, agent interaction, dynamic scoring) that aligns with human ratings on 13 frontier LLMs.
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WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games
WebGameBench is a new benchmark that evaluates coding agents on building browser-native games from frozen specifications, with runtime browser evaluation showing best agents reach 76.9% usable rate but only 20.2% excellent rate.