Explore the latest vision–language model (VLM) research papers (2025) with concise, accessible summaries. This section covers key breakthroughs in multimodal AI, including image–text understanding, visual question answering, image captioning, cross-modal retrieval, and large-scale VLM architectures.
Each summary highlights contributions, methods, and results, with direct links to the original research. Stay informed on the most important multimodal AI papers without needing to read lengthy PDFs.
Qwen-Image is a large-scale vision–language model designed for multimodal understanding. By combining image recognition with text generation, it achieves strong results on benchmarks for captioning, retrieval, and reasoning, offering a powerful foundation for multimodal AI research.
InternVL3.5: Open-Source Multimodal Model
InternVL3.5 is an open-source vision–language model that integrates large-scale visual and textual understanding. It shows strong performance in tasks such as captioning, cross-modal retrieval, and VQA, pushing forward multimodal reasoning in research and practical AI systems.
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Efficient hybrid encoder for Vision-Language Models that slashes encoding latency—up to 85× faster time-to-first-token, 3.4× smaller encoder—while retaining accuracy. Ideal for real-time, on-device VLM applications.
