arXiv AI August 5, 2026 By Khai-Nguyen Nguyen, Oscar Chaparro, Antonio Mastropaolo

Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation

arXiv:2608. 03691v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway them toward visually incorrect yet pattern-consistent outputs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv Computation and Language

Aug 21

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models

arXiv:2608. 19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored.

By Yinfeng Wang, Zhiyuan Yao, Zheren Fu, Lei Zhang, Zhendong Mao

llms multimodal benchmarks safety

arXiv AI

3d ago

VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes

arXiv:2605.26380v2 Announce Type: replace-cross Abstract: Frontier multimodal large language models (MLLMs) have been reported to achieve over 90\% accuracy on fine-grained perception benchmarks. How...

By Jingru Chen, Yiming Liu, Mingtao Chen, Sijie Chen, Richeng Xuan, Liang Yang, Zhichao Hu, Fanyang Lu

llms multimodal benchmarks

arXiv Machine Learning

Sep 24

ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text

ASCIIBench is a new benchmark that evaluates large language models on generating and classifying ASCII-text images, using a dataset of 5,315 labeled ASCII images. The authors also release a fine‑tuned CLIP model adapted to capture ASCII structure for evaluation. Their analysis shows that cosine similarity on CLIP embeddings fails to separate most categories, indicating a representation bottleneck rather than generational variance.

By Kerry Luo, Michael Fu, Joshua Peguero, Husnain Malik, Anvay Patil, Joyce Lin, Megan Van Overborg, Ryan Sarmiento, Kevin Zhu

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 2

ProductWebGen: Benchmarking Multimodal Product Webpage Generation

arXiv:2606. 01022v1 Announce Type: cross Abstract: Crafting a product display webpage from a source product image, along with layout and visual content instructions, holds significant practical value for domains such as marketing, advertising, and E-commerce.

By Zhihong Liu, Siqi Kou, Zheng Li, Ye Ma, Quan Chen, Peng Jiang, Kai Yu, Zhijie Deng

llms diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 30

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation

arXiv:2509. 12159v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency.

By Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan, Yintong Huo, Yang Liu, Michael R. Lyu

llms multimodal

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Related stories

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models

VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes

ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text

ProductWebGen: Benchmarking Multimodal Product Webpage Generation

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation