Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Inclusion AI, Tiwei Bie, Haoxing Chen +15 authors
LLaDA2.0-Uni is a unified discrete diffusion language model that integrates multimodal understanding and generation through a semantic discrete tokenizer, MoE-based backbone, and diffusion decoder, achieving performance comparable to specialized vision-language models while enabling efficient inference and high-fidelity image generation.
Dong Guo, Faming Wu, Feida Zhu +194 authors
Seed1.5-VL, a vision-language foundation model combining a vision encoder and a large MoE LLM, achieves state-of-the-art performance across various benchmarks and excels in multimodal reasoning tasks such as visual puzzles.
Chaorui Deng, Deyao Zhu, Kunchang Li +9 authors
BAGEL, an open-source foundational model trained on diverse multimodal data, significantly outperforms existing models in both generation and understanding tasks.
Boqiang Zhang, Lei Ke, Ruihan Yang +5 authors
Penguin-VL demonstrates that text-only initialized vision encoders can achieve superior performance in multimodal understanding tasks compared to traditional contrastive pretraining methods, enabling efficient deployment on resource-constrained devices.
Dongxu Li, Yudong Liu, Haoning Wu +7 authors
Aria is an open multimodal native AI model with best-in-class performance across various tasks, designed with a mixture-of-experts architecture and pre-trained through a four-stage pipeline.
Kevin Qinghong Lin, Yuhao Zheng, Hangyu Ran +5 authors
VCode introduces a benchmark for generating SVG code from images to preserve symbolic meaning, highlighting gaps in visual-centric coding and proposing VCoder to improve performance.
Ling Yang, Ye Tian, Bowen Li +4 authors
MMaDA, a multimodal diffusion foundation model, achieves superior performance through a unified architecture, mixed long chain-of-thought fine-tuning, and a unified policy-gradient-based RL algorithm.
Xinjie Zhang, Jintao Guo, Shanshan Zhao +7 authors
A survey examines the integration of multimodal understanding and image generation models, categorizing them into diffusion-based, autoregressive-based, and hybrid approaches, and discussing challenges and future research directions.
Zhiheng Liu, Weiming Ren, Haozhe Liu +22 authors
TUNA, a unified multimodal model, uses a cascaded VAE and representation encoder for end-to-end multimodal understanding and generation, outperforming decoupled models and achieving state-of-the-art results across various benchmarks.
Mistral-AI, Abhinav Rastogi, Albert Q. Jiang +97 authors
Magistral, a scalable reinforcement learning pipeline, demonstrates that RL can enhance multimodal understanding and instruction following in large language models without requiring existing RL traces.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3300 authors
Gemini 2.X model family, including Gemini 2.5 Pro and Flash, offers superior coding, reasoning, and multimodal understanding capabilities across a range of computational efficiencies.
Guo-Hua Wang, Shanshan Zhao, Xinjie Zhang +9 authors
Ovis-U1, a 3-billion-parameter unified model, integrates multimodal understanding, text-to-image generation, and image editing using a diffusion-based visual decoder and bidirectional token refiner, achieving state-of-the-art performance across various benchmarks.
Xiaoyi Dong, Pan Zhang, Yuhang Zang +20 authors
InternLM-XComposer2, a vision-language model, excels in free-form text-image creation and understanding using Partial LoRA, surpassing existing models and matching GPT-4V and Gemini Pro in multimodal benchmarks.
Jinheng Xie, Weijia Mao, Zechen Bai +7 authors
Show-o, a unified transformer model, combines autoregressive and diffusion modeling for versatile vision-language tasks, achieving competitive performance.
Zhekai Chen, Chengqi Duan, Kaiyue Sun +4 authors
UniClawBench introduces a capability-driven benchmark for evaluating proactive agents in real-world environments using live Docker container evaluation and closed-loop assessment with multiple agent roles.
Yi Wang, Yinan He, Yizhuo Li +10 authors
InternVid, a large-scale video-text multimodal dataset, and ViCLIP, a video-text representation model based on ViT-L, enable advanced video and text understanding and generation tasks.
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