22 citations · 32 across the 27 of their papers we have counts for
29 papers
CoViT: Instance-Correspondence Contrastive Learning for Vision Transformer
Yisen Wang, Zhirong Wu, Limin Wang
Vision Transformers (ViT) excel in semantic understanding but fail to discriminate between object instances (e.g., identical embeddings for two dogs), limiting their use in instanc…
Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning
Lizhe Fang, Weizhou Shen, Tianyi Tang +1
Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an i…
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
Yi-Siang Wang, Kuan-Yu Chen, Yu-Chen Den +1
Large language models (LLMs) have recently been adapted to tabular prediction by serializing structured features into natural language, but their performance in low-data regimes re…
SAE as a Crystal Ball: Interpretable Features Predict Cross-domain Transferability of LLMs without Training
Qi Zhang, Yifei Wang, Xiaohan Wang +4
In recent years, pre-trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self-supervised pre-training, their effectiven…
Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents
Qirui Mi, Zhijian Ma, Mengyue Yang +4
LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. This insufficient experience reuse…
Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation
Tianqi Du, Lizhe Fang, Weijie Yang +4
Diffusion language models enable any-order generation and bidirectional conditioning, offering appealing flexibility for tasks such as infilling, rewriting, and self-correction. Ho…