4 citations · 12 across the 21 of their papers we have counts for
23 papers
Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models
Yuanhao Sun, Huawei Ji, Jiaxin Ding +2
A central goal of vision-language model (VLM) distillation is to transfer both the teacher's language capabilities and its visual understanding. However, existing methods primarily…
GRAND-HC: Graph-Refined Author Name Disambiguation
Yuanhao Sun, Zhouyang Jin, Yi Xu +5
From-Scratch Name Disambiguation (SND) groups papers sharing an ambiguous name into clusters of distinct real-world authors. Existing methods suffer from two critical limitations:…
ENCORE: Entropy-Guided Cropping and Attention Regularization for Robust Vision--Language Understanding
Yuanhao Sun, Huawei Ji, Jiaxin Ding +2
Vision-Language Models (VLMs) perform well on diverse vision-language tasks, but transformer-based visual encoders split images into fixed-resolution sub-images, compromising objec…
Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning
Yijun Zhang, Yule Xie, Jiaxin Ding +4
Reinforcement learning has become a central paradigm for improving the reasoning capabilities of large language models. Existing methods generally aim to reduce the failure probabi…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn Search Agents
Yijun Zhang, Fan Xu, Jiaxin Ding +6
Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome. However, ro…
VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models
Huawei Ji, Yuanhao Sun, Yuan Jin +4
Visual token pruning methods effectively mitigate the quadratic computational growth caused by processing high-resolution images and video frames in vision-language models (VLMs).…
RADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning
Bo Xue, Yuan Jin, Luoyi Fu +2
Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). Howe…
Extreme Value Policy Optimization for Safe Reinforcement Learning
Shiqing Gao, Yihang Zhou, Shuai Shao +5
Ensuring safety is a critical challenge in applying Reinforcement Learning (RL) to real-world scenarios. Constrained Reinforcement Learning (CRL) addresses this by maximizing retur…
Controlling Underestimation Bias in Constrained Reinforcement Learning for Safe Exploration
Shiqing Gao, Jiaxin Ding, Luoyi Fu +1
Constrained Reinforcement Learning (CRL) aims to maximize cumulative rewards while satisfying constraints. However, existing CRL algorithms often encounter significant constraint v…
Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression
Zijun Di, Bin Lu, Huquan Kang +5
Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding. Recent studies typically focus on verbalizing the graph structur…
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
Yutong Zhang, Lixing Chen, Shenghong Li +6
Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks nece…
Scientific and technological knowledge grows linearly over time
Huquan Kang, Luoyi Fu, Russell J. Funk +6
The past few centuries have witnessed a dramatic growth in scientific and technological knowledge. However, the nature of that growth - whether exponential or otherwise - remains c…
Good Idea or Not, Representation of LLM Could Tell
Yi Xu, Bo Xue, Shuqian Sheng +6
In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful…
AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing
Huawei Ji, Cheng Deng, Bo Xue +6
With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predomin…
FLAME: Empowering Frozen LLMs for Knowledge Graph Completion
Bo Xue, Yi Xu, Bolei Ma +4
Traditional knowledge graph completion (KGC) methods rely solely on structural information and struggle with sparsity, while Large Language Models (LLMs) address these limitations…
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints
Shiqing Gao, Jiaxin Ding, Luoyi Fu +2
In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been…
AceMap: Knowledge Discovery through Academic Graph
Xinbing Wang, Luoyi Fu, Xiaoying Gan +23
The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering sear…
Characterizing the Influence of Topology on Graph Learning Tasks
Kailong Wu, Yule Xie, Jiaxin Ding +4
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the…
RepEval: Effective Text Evaluation with LLM Representation
Shuqian Sheng, Yi Xu, Tianhang Zhang +7
The era of Large Language Models (LLMs) raises new demands for automatic evaluation metrics, which should be adaptable to various application scenarios while maintaining low cost a…
Is Reference Necessary in the Evaluation of NLG Systems? When and Where?
Shuqian Sheng, Yi Xu, Luoyi Fu +4
The majority of automatic metrics for evaluating NLG systems are reference-based. However, the challenge of collecting human annotation results in a lack of reliable references in…
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
Cheng Deng, Jiaxin Ding, Luoyi Fu +3
The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on b…
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Qi Li, Yuyang Ren, Xingli Wang +5
Understanding the origin and influence of the publication's idea is critical to conducting scientific research. However, the proliferation of scientific publications makes it diffi…
Disentangled Graph Contrastive Learning for Review-based Recommendation
Yuyang Ren, Haonan Zhang, Qi Li +5
User review data is helpful in alleviating the data sparsity problem in many recommender systems. In review-based recommendation methods, review data is considered as auxiliary inf…