6 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
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…
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…
Valley: Video Assistant with Large Language model Enhanced abilitY
Ruipu Luo, Ziwang Zhao, Min Yang +6
Large Language Models (LLMs), with remarkable conversational capability, have emerged as AI assistants that can handle both visual and textual modalities. However, their effectiven…
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…
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…