TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models

Zhenkun Gao, Xuhong Wang, Xin Tan, Yuan Xie

International Conference on Learning Representations (ICLR) 2026

[Paper] [Code]

We build TPRU, a large-scale high-quality dataset for temporal and procedural understanding with three complementary tasks (temporal reordering, next-frame prediction, and previous-frame recall), and fine-tune Qwen2.5-VL (3B/7B/32B) with GRPO-based reinforcement learning. TPRU-7B achieves significant gains on public multi-image benchmarks such as MUIRBench and LEGO-Puzzles, boosting TPRU-Test accuracy from 50.33% to 75.70%.

VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning

Zhenkun Gao*, Yicheng Bao*, Jinlong Peng*, Xueheng Li*, Theo Huang*, Bangwei Liu, Kunquan Li, Zhenye Gan, Tao Hu, Chengjun Xie, Mingqian Yang, Xuanhua He, Zhizhong Zhang, Xin Tan, Chengjie Wang, Yuan Xie

Technical Report

[Paper] [Code]

We propose VideoSearcher, a closed-loop multi-tool agent framework for Video Deep Research, which unifies key-frame localization, local zoom-in, image search, and web search for video clue grounding, open-web retrieval, and evidence integration. We further develop a video-centric training data pipeline and BiSPO, a dual-branch sequence-level RL algorithm that decouples answer-accuracy optimization from tool-use behavior optimization. VideoSearcher-8B reaches an average score of 57.66% on 8 search-oriented benchmarks, outperforming the Qwen3-VL-8B agentic baseline by 15.71% on average.

Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration

Sen Wang, Bangwei Liu, Zhenkun Gao, Lizhuang Ma, Xuhong Wang, Yuan Xie, Xin Tan

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026

[Paper] [Code]

We develop MemoryExplorer, a multimodal LLM-based embodied exploration framework targeting memory maintenance, environment exploration, and decision planning in long-horizon complex tasks. The model is trained with GRPO-based reinforcement learning to actively leverage long-term memory, unifying cognition, memory, and decision-making of embodied agents. I mainly contributed to building the core evaluation pipeline and writing the experimental sections.

Towards Safe and Trustworthy Embodied AI: Foundations, Status, and Prospects

Xin Tan, Bangwei Liu, Yicheng Bao, Qijian Tian, Zhenkun Gao, Xiongbin Wu, Zhihao Luo, Sen Wang, Yuqi Zhang, Xuhong Wang, Chaochao Lu, Bowen Zhou

Position Paper

[Paper] [News]

A position paper on safe and trustworthy Embodied AI (EAI). We deconstruct the workflow of embodied agents into four core stages — instruction understanding, environmental perception, behavior planning, and physical interaction — systematically review the safety and trustworthiness challenges at each stage, and propose a five-level maturity roadmap towards proactive, intrinsically safe EAI systems.