10 papers
Valley3: Scaling Omni Foundation Models for E-commerce
Zeyu Chen, Guanghao Zhou, Qixiang Yin +6
In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified understanding and reasoning capabilitie…
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
Yifan Li, Yukai Gu, Yingqian Min +6
Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation…
Unleashing Perception-Time Scaling to Multimodal Reasoning Models
Yifan Li, Zhenghao Chen, Ziheng Wu +7
Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of L…
VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?
Jiachen Yu, Yufei Zhan, Ziheng Wu +3
Recent extensive works have demonstrated that by introducing long CoT, the capabilities of MLLMs to solve complex problems can be effectively enhanced. However, the reasons for the…
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
Zhentao He, Can Zhang, Ziheng Wu +6
Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompl…
MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Wei Chow, Yuan Gao, Linfeng Li +15
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular r…
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Yufei Zhan, Ziheng Wu, Yousong Zhu +10
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general sc…
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
Huanjin Yao, Qixiang Yin, Jingyi Zhang +8
In this work, we aim to incentivize the reasoning ability of Multimodal Large Language Models (MLLMs) via reinforcement learning (RL) and develop an effective approach that mitigat…
Valley2: Exploring Multimodal Models with Scalable Vision-Language Design
Ziheng Wu, Zhenghao Chen, Ruipu Luo +6
Recently, vision-language models have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introdu…
VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models
Zejun Li, Ruipu Luo, Jiwen Zhang +3
While large multi-modal models (LMMs) have exhibited impressive capabilities across diverse tasks, their effectiveness in handling complex tasks has been limited by the prevailing…