1 citations · 2 across the 7 of their papers we have counts for
7 papers
DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning
Lingyong Yan, Can Xu, Yukun Zhao +13
Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, veri…
ProCrit: Self-Elicited Multi-Perspective Reasoning with Critic-Guided Revision for Multimodal Sarcasm Detection
Yingjia Xu, Jiulong Wu, Bowen Zhang +3
Multimodal sarcasm detection requires reasoning over cross-modal incongruities between literal expression and intended meaning, yet the specific analytical perspectives needed vary…
VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning
Yucheng Shen, Jiulong Wu, Jizhou Huang +3
Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex queries requiring multi-step rea…
Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation
Lingyong Yan, Jiulong Wu, Dong Xie +3
Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict lo…
Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis
Jiulong Wu, Yucheng Shen, Lingyong Yan +4
Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrates three subtasks: emotion recog…
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization
Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang +5
Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challenged by hallucinations, which ca…
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Weiwei Sun, Zhengliang Shi, Jiulong Wu +6
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially…