activity
20242026
most citedBeyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.AI2026

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…

cs.CV2026

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…

cs.AI20261 cited

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…

cs.MA2026

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…

cs.CV2026

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…

cs.CV2025

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…