most citedComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning

Xinchen Ma, Shuimu Wang, Gaole He +4

Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than supporting learning. As a resu…

cs.CV2026

InsightVQA: High-Dimensional Emotion-Cognitive Visual Question Answering Benchmark

Shiyu Wang, Ziyu Liu, Chaoyi Yu +6

Visual emotion understanding requires models not only to recognize emotional states, but also to why they arise and perform higher-level cognitive reasoning. However, existing benc…

cs.CV2026

ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception

Huanzhen Wang, Ziheng Zhou, Jiaqi Song +4

Dynamic facial expression recognition in the wild remains challenging due to data scarcity and long-tail distributions, which hinder models from effectively learning the temporal d…

cs.CL2026

Unsupervised Text Style Transfer for Controllable Intensity

Shuhuan Gu, Wenbiao Tao, Xinchen Ma +4

Unsupervised Text Style Transfer (UTST) aims to build a system to transfer the stylistic properties of a given text without parallel text pairs. Compared with text transfer between…

cs.IR2026

TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation

Wenbiao Tao, Xinyuan Li, Yunshi Lan +1

Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses. However, traditional RAG methods rely on fragme…

cs.CL2025

RIDE: Difficulty Evolving Perturbation with Item Response Theory for Mathematical Reasoning

Xinyuan Li, Murong Xu, Wenbiao Tao +4

Large language models (LLMs) achieve high performance on mathematical reasoning, but these results can be inflated by training data leakage or superficial pattern matching rather t…

cs.CL2025★ 1 cited

ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu +5

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in rea…