Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots—before, during, and after a scripted interaction—then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95% referee–human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4% normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/.

This paper studies vision-language (V&L) pre-training for deep cross-modal representations. Recently, pre-trained V&L models have shown great success in V&L tasks. However, most existing models apply multi-modal encoders to encode the image and text, at the cost of high training complexity because of the input sequence length. In addition, they suffer from noisy training corpora caused by V&L mismatching. In this work, we propose a lightweight vision-language pre-training (LightVLP) for efficient and effective V&L pre-training. First, we design a new V&L framework with two autoencoders. Each autoencoder involves an encoder, which only takes in unmasked tokens (removes masked ones), as well as a lightweight decoder that reconstructs the masked tokens. Besides, we mask and remove large portions of input tokens to accelerate the training. Moreover, we propose a gated interaction mechanism to cope with noise in aligned image-text pairs. As for a matched image-text pair, the model tends to apply cross-modal representations for reconstructions. By contrast, for an unmatched pair, the model conducts reconstructions mainly using uni-modal representations. Benefiting from the above-mentioned designs, our base model shows competitive results compared to ALBEF while saving 44% FLOPs. Further, we compare our large model with ALBEF under the setting of similar FLOPs on six datasets and show the superiority of LightVLP. In particular, our model achieves 2.2% R@1 gains on COCO Text Retrieval and 1.1% on refCOCO+.