most citedKAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks

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

collaborators

5 papers

cs.AI2025

Through the Judge's Eyes: Inferred Thinking Traces Improve Reliability of LLM Raters

Xingjian Zhang, Tianhong Gao, Suliang Jin +4

Large language models (LLMs) are increasingly used as raters for evaluation tasks. However, their reliability is often limited for subjective tasks, when human judgments involve su…

cs.CL2025

Benchmarking and Learning Real-World Customer Service Dialogue

Tianhong Gao, Jundong Shen, Jiapeng Wang +4

Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) remain misaligned with real-world dialogue requirements, overemphasizing verifiable tas…

cs.CV2025

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning

Tianhong Gao, Yannian Fu, Weiqun Wu +3

Large Language Models (LLMs), enhanced through agent tuning, have demonstrated remarkable capabilities in Chain-of-Thought (CoT) and tool utilization, significantly surpassing the…

cs.LG2025★ 1 cited

KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks

Taoran Fang, Tianhong Gao, Chunping Wang +4

Graph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years. However, o…

cs.CV2024

Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis

Shengxuming Zhang, Weihan Li, Tianhong Gao +6

Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resol…