UI2App introduces a benchmark showing that vision-language models can reconstruct web page visuals but largely fail to infer the underlying interaction logic from screenshots alone.
Artifactsbench: Bridging the visual-interactive gap in llm code generation evaluation
11 Pith papers cite this work. Polarity classification is still indexing.
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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.
Introduces WorldCoder-Bench and StateProbe for evaluating LLM-generated physically grounded 3D browser worlds, with frontier models reaching at most 27.8% verification coverage.
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.
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.
FlowEval evaluates generated UIs by measuring how closely their navigation flows match real websites via reference-based similarity metrics and shows strong correlation with human expert judgments.
uxCUA is a trained computer use agent that assesses GUI usability more accurately than larger models by learning to prioritize and execute important user interactions on labeled interface datasets.
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.
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.
A structured survey of multimodal code intelligence that formulates the field by code roles and organizes work into four domains while proposing verification-centered research directions.
Seed2.0 model series reports gains in reasoning, visual understanding, search, and reliability on intricate long-horizon tasks via an internal evaluation system.
citing papers explorer
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UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
UI2App introduces a benchmark showing that vision-language models can reconstruct web page visuals but largely fail to infer the underlying interaction logic from screenshots alone.
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Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement
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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WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis
Introduces WorldCoder-Bench and StateProbe for evaluating LLM-generated physically grounded 3D browser worlds, with frontier models reaching at most 27.8% verification coverage.
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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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From Runnable to Shippable: Multi-Agent Test-Driven Development for Generating Full-Stack Web Applications from Requirements
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.
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FlowEval: Reference-based Evaluation of Generated User Interfaces
FlowEval evaluates generated UIs by measuring how closely their navigation flows match real websites via reference-based similarity metrics and shows strong correlation with human expert judgments.
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Training Computer Use Agents to Assess the Usability of Graphical User Interfaces
uxCUA is a trained computer use agent that assesses GUI usability more accurately than larger models by learning to prioritize and execute important user interactions on labeled interface datasets.
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WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
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.
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Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
This survey organizes RL for LLM multi-agent systems into reward families, credit units, and five orchestration sub-decisions, notes the absence of explicit stopping-decision training in its paper pool, and releases a tagged corpus.
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Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
A structured survey of multimodal code intelligence that formulates the field by code roles and organizes work into four domains while proposing verification-centered research directions.
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Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
Seed2.0 model series reports gains in reasoning, visual understanding, search, and reliability on intricate long-horizon tasks via an internal evaluation system.