2 citations · 3 across the 6 of their papers we have counts for
7 papers
Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe
Xixi Wu, Qianguo Sun, Ruiyang Zhang +4
Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in…
VSearcher: Long-Horizon Multimodal Search Agent via Reinforcement Learning
Ruiyang Zhang, Qianguo Sun, Chao Song +2
Large models are increasingly becoming autonomous agents that interact with real-world environments and use external tools to augment their static capabilities. However, most recen…
SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models
Ruiyang Zhang, Dongzhan Zhou, Zhedong Zheng
Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., i…
SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing
Ruiyang Zhang, Jiahao Luo, Xiaoru Feng +3
With the rapid advancement of text-to-image (T2I) models, ensuring their safety has become increasingly critical. Existing safety approaches can be categorized into training-time a…
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Xixi Wu, Yanchao Tan, Nan Hou +2
Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the do…
Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models
Ruiyang Zhang, Hu Zhang, Hao Fei +1
Large Multimodal Models (LMMs), harnessing the complementarity among diverse modalities, are often considered more robust than pure Language Large Models (LLMs); yet do LMMs know w…