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

16 papers

cs.AI2026

MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation

Deguo Xia, Zihan Li, Haochen Zhao +6

Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities rem…

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.AI2026

Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models

Dayu Wang, Jiaye Yang, Weikang Li +4

Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in interme…

cs.SE2026

One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents

Zhaoxi Zhang, Yitong Duan, Yanzhi Zhang +9

Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods typically…

cs.DB2026

SciEGQA: A Dataset for Scientific Evidence-Grounded Question Answering and Reasoning

Wenhan Yu, Zhaoxi Zhang, Wang Chen +5

Scientific documents contain complex multimodal structures, which makes evidence localization and scientific reasoning in Document Visual Question Answering particularly challengin…