1 citations · 1 across the 9 of their papers we have counts for
9 papers
Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Ziyi Wang, Xumin Yu, Yongming Rao +19
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situatio…
HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents
Tencent Robotics X, HY Vision Team, : +20
We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) an…
Flow Matching for Offline Reinforcement Learning with Discrete Actions
Fairoz Nower Khan, Nabuat Zaman Nahim, Ruiquan Huang +2
Generative policies based on diffusion models and flow matching have shown strong promise for offline reinforcement learning (RL), but their applicability remains largely confined…
UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist
Zhengyang Liang, Daoan Zhang, Huichi Zhou +8
While specialized AI models excel at isolated video tasks like generation or understanding, real-world applications demand complex, iterative workflows that combine these capabilit…
Disc3D: Automatic Curation of High-Quality 3D Dialog Data via Discriminative Object Referring
Siyuan Wei, Chunjie Wang, Xiao Liu +3
3D Multi-modal Large Language Models (MLLMs) still lag behind their 2D peers, largely because large-scale, high-quality 3D scene-dialogue datasets remain scarce. Prior efforts hing…
Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning
Rui Jerry Huang, Wendy Liu, Anastasia Miin +1
Inference-time computation is a critical yet challenging paradigm for enhancing the reasoning performance of large language models (LLMs). While existing strategies improve reasoni…
Video-STR: Reinforcing MLLMs in Video Spatio-Temporal Reasoning with Relation Graph
Wentao Wang, Heqing Zou, Tianze Luo +8
Recent progress in Multimodal Large Language Models (MLLMs) has demonstrated strong semantic understanding capabilities, but struggles to perform precise spatio-temporal understand…
OneRec-V2 Technical Report
Guorui Zhou, Hengrui Hu, Hongtao Cheng +72
Recent breakthroughs in generative AI have transformed recommender systems through end-to-end generation. OneRec reformulates recommendation as an autoregressive generation task, a…
OneRec Technical Report
Guorui Zhou, Jiaxin Deng, Jinghao Zhang +62
Recommender systems have been widely used in various large-scale user-oriented platforms for many years. However, compared to the rapid developments in the AI community, recommenda…