4 citations · 4 across the 9 of their papers we have counts for
9 papers
Prisma-World: Camera-Controllable Multi-Agent Video World Model
Huiqiang Sun, Zhan Peng, Size Wu +9
Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single observer. Extending such models t…
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
Jinyang Du, Shenghao Jin, Ziqian Xu +5
Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint…
Video-KTR: Reinforcing Video Reasoning via Key Token Attribution
Ziyue Wang, Sheng Jin, Zhongrong Zuo +5
Reinforcement learning (RL) has shown strong potential for enhancing reasoning in multimodal large language models, yet existing video reasoning methods often rely on coarse sequen…
Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge
Li Kang, Heng Zhou, Xiufeng Song +41
Recent advancements in multimodal large language models and vision-languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more com…
JavisGPT: A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
Kai Liu, Jungang Li, Yuchong Sun +13
This paper presents JavisGPT, the first unified multimodal large language model (MLLM) for joint audio-video (JAV) comprehension and generation. JavisGPT has a concise encoder-LLM-…
Linear Dynamics in the RLVR Training of Large Language Models
Tianle Wang, Jiayu Liu, Zhongyuan Wu +4
Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LLMs), yet its internal training dynamic…
Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics
Sheng Jin, Haoming Wang, Zhiqi Gao +3
Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) syst…
OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation
Size Wu, Zhonghua Wu, Zerui Gong +5
In this report, we present OpenUni, a simple, lightweight, and fully open-source baseline for unifying multimodal understanding and generation. Inspired by prevailing practices in…
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Sicheng Yu, Chengkai Jin, Huanyu Wang +9
Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it…