6 papers
NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons
Haonan Dong, Kehan Jiang, Haoran Ye +3
Large Reasoning Models (LRMs) have recently achieved remarkable success in complex reasoning tasks. However, closer scrutiny reveals persistent failure modes compromising performan…
Meta-R1: Empowering Large Reasoning Models with Metacognition
Haonan Dong, Haoran Ye, Wenhao Zhu +2
Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex tasks, exhibiting emergent, human-like thinking patterns. Despite their advances, we identify a fundame…
EAVIT: Efficient and Accurate Human Value Identification from Text data via LLMs
Wenhao Zhu, Yuhang Xie, Guojie Song +1
The rapid evolution of large language models (LLMs) has revolutionized various fields, including the identification and discovery of human values within text data. While traditiona…
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
Haonan Dong, Wenhao Zhu, Guojie Song +1
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method validated across NLP and CV domains. However, LoRA faces an inherent low-rank bottlenec…
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers
Wenhao Zhu, Guojie Song, Liang Wang +1
Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and de…
A parameter-free clustering algorithm for missing datasets
Qi Li, Xianjun Zeng, Shuliang Wang +3
Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute…