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。
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16 authors
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness.
Yujia Zhang, Xiaoyang Wu, Yunhan Yang +6 authors
Utonia enables cross-domain point cloud representation learning through a unified self-supervised transformer encoder, enhancing perception and supporting embodied and multimodal reasoning tasks.
Shuai Bai, Yuxuan Cai, Ruizhe Chen +61 authors
Qwen3-VL, a vision-language model, excels in text and multimodal understanding through advanced architectures and larger contexts, achieving superior performance across benchmarks.
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.
Jin Xu, Zhifang Guo, Hangrui Hu +35 authors
Qwen3-Omni, a multimodal model, achieves state-of-the-art performance across text, image, audio, and video, using a Thinker-Talker MoE architecture and a lightweight causal ConvNet for efficient streaming synthesis.
Shiyin Lu, Yang Li, Yu Xia +39 authors
Ovis2.5, a native-resolution vision transformer with multimodal reasoning, achieves state-of-the-art performance on various benchmarks through advanced training techniques and efficient scaling methods.
Kaichen Zhang, Keming Wu, Zuhao Yang +5 authors
OpenMMReasoner, a two-stage training approach combining supervised fine-tuning and reinforcement learning, enhances multimodal reasoning performance through rigorous data curation and improved training strategies.
Zhaochen Su, Peng Xia, Hangyu Guo +12 authors
The survey outlines the evolution of multimodal AI from treating vision as static context to integrating it dynamically into the reasoning process, highlighting three stages and key challenges.
Yingzhe Peng, Gongrui Zhang, Miaosen Zhang +7 authors
A two-stage framework enhances reasoning in large multimodal models by first strengthening text-only reasoning and then generalizing it to multimodal tasks.
Jiawei Gu, Yunzhuo Hao, Huichen Will Wang +5 authors
ThinkMorph, a unified model fine-tuned on interleaved reasoning traces, enhances multimodal reasoning by generating coherent text-image steps, achieving significant performance gains and demonstrating emergent capabilities.
Jiaqi Tang, Jianmin Chen, Youyang Zhai +6 authors
Robust-U1 enhances multimodal large language models' robustness against visual corruptions through self-recovery capabilities that improve both visual quality and reasoning performance.
Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin +71 authors
MiMo-VL-7B-SFT and MiMo-VL-7B-RL provide state-of-the-art general visual understanding and multimodal reasoning through four-stage pre-training and Mixed On-policy Reinforcement Learning, outperforming models with up to 78B parameters.
Yiheng Xu, Zekun Wang, Junli Wang +6 authors
Aguvis is a unified, vision-based framework for autonomous GUI agents that integrates language grounding and explicit planning, enabling cross-platform generalization and outperforming existing methods.
Fanqing Meng, Lingxiao Du, Zongkai Liu +11 authors
MM-Eureka extends rule-based reinforcement learning to multimodal reasoning, achieving strong capabilities in data-efficient multimodal tasks without supervised fine-tuning.
Hengguang Zhou, Xirui Li, Ruochen Wang +3 authors
A non-SFT model replicated emergent reasoning characteristics for multimodal tasks using reinforcement learning, achieving higher accuracy than base and SFT models on CVBench.
Chenlong Wang, Yuanning Feng, Dongping Chen +3 authors
NoWait suppresses explicit self-reflection tokens during inference to enhance efficiency in multimodal reasoning without reducing model utility.
Di Zhang, Jingdi Lei, Junxian Li +10 authors
The Critic-V framework leverages an Actor-Critic paradigm to enhance multimodal reasoning in vision-language models by decoupling reasoning and critique processes, significantly improving performance and reliability.
Tsung-Han Wu, Heekyung Lee, Anya Ji +4 authors
CoVA-SFT is a multimodal reasoning dataset that teaches language models to interleave text and visual abstractions via structured reasoning steps, improving performance on visual reasoning benchmarks.
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