arXiv AI
WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 2
Benchmarking Multimodal LLMs on Code Generation for Complex Interactive Webpages
arXiv:2606. 00154v1 Announce Type: cross Abstract: Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-end development.
arXiv Computation and Language
Oct 28
FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow
FullFront is a new benchmark that evaluates Multimodal Large Language Models (MLLMs) across the entire front‑end engineering workflow, covering webpage design, perception QA, and code generation. It uses a two‑stage process to convert real‑world webpages into clean, standardized HTML, avoiding copyright issues and providing diverse visual designs. Experiments show that current MLLMs struggle with page perception, image handling, layout, and interaction implementation, revealing a large gap compared to human experts.
arXiv Computer Vision
Sep 3
Rendering-in-the-Loop: An Execution-Driven Agent for Interactive Web Development
RILA is an execution‑driven agent that integrates browser rendering into the generation loop for interactive web development. It uses an Action Interaction Verification module to replay reference interactions on generated pages, collecting execution‑aware observations, and an Execution‑aware Rendering Score to jointly assess interaction correctness and visual fidelity during iterative optimization. A data synthesis pipeline further augments training data, enabling RILA to significantly improve interaction and visual quality across foundation models, even outperforming larger one‑shot generators.
arXiv AI
1d ago
RefCraft: Benchmarking Visual-Reference-Conditioned Agentic Generation through Web Development
arXiv:2610.26826v2 Announce Type: replace-cross Abstract: Beyond basic tool use and coding skills, effectively incorporating visual references into agentic generation is equally essential for applyin...
arXiv Machine Learning
Oct 15
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
arXiv:2610.20538v1 Announce Type: cross Abstract: Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is bu...
arXiv Computer Vision
Oct 15
VisualWorldBench: A Fine-Grained Multi-Task Benchmark for Evaluating Visual World Knowledge in MLLMs
arXiv:2610.20083v1 Announce Type: new Abstract: Visual world knowledge is a core capability of multimodal large language models (MLLMs), underpinning knowledge-intensive visual question answering and...