6 papers
VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning
Zhenkun Gao, Yicheng Bao, Jinlong Peng +13
Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR). However, existing multimo…
VRPRM: Process Reward Modeling via Visual Reasoning
Xinquan Chen, Chongying Yue, Bangwei Liu +3
Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) because it can perform fine-grained evaluation of the reasoning steps of generated cont…
Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
Xinquan Chen, Zhenyun Yin, Shan He +38
As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment intera…
Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration
Sen Wang, Bangwei Liu, Zhenkun Gao +4
An ideal embodied agent should possess lifelong learning capabilities to handle long-horizon and complex tasks, enabling continuous operation in general environments. This not only…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
IDMR: Towards Instance-Driven Precise Visual Correspondence in Multimodal Retrieval
Bangwei Liu, Yicheng Bao, Shaohui Lin +5
Multimodal retrieval systems are becoming increasingly vital for cutting-edge AI technologies, such as embodied AI and AI-driven digital content industries. However, current multim…