activity
20162025
most citedMeasuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

80 citations · 557 across the 56 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

physics.chem-ph2025

Exceptional Alkaline Methanol Electrooxidation on Bi-modified Pt3M Intermetallics: Kinetic Origins and an OH Binding Energy Descriptor

Lecheng Liang, Hengyu Li, Shao Ye +7

The exploration of advanced CO-free catalysts and clarifying the ambiguous kinetic origins and governing factors would undoubtedly open up opportunities to overcome the sluggish ki…

cs.AI2025

AI Mathematician: Towards Fully Automated Frontier Mathematical Research

Yuanhang Liu, Yanxing Huang, Yanqiao Wang +2

Large Reasoning Models (LRMs) have made significant progress in mathematical capabilities in recent times. However, these successes have been primarily confined to competition-leve…

cs.CL2025★ 3 cited

AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization

Yiyang Du, Xiaochen Wang, Chi Chen +9

Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model mergin…

cs.CV2025

CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models

Yiqi Zhu, Ziyue Wang, Can Zhang +2

Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows, VLMs can now comprehend a broad…

cs.CV2025

LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents

Boyu Chen, Zhengrong Yue, Siran Chen +4

Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single…

cs.LG2025

Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion

Tianyuan Zou, Yang Liu, Peng Li +6

Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that res…