2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
Songtao Wei, Yi Li, Zhikai Li +7
Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated Chain-of-Thought (CoT) trajec…
LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization
Tao Li, Kaiyuan Hou, Tuan Vinh +4
Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic pathways. Existing synthesis-aware met…
CoSA: Compressed Sensing-Based Adaptation of Large Language Models
Songtao Wei, Yi Li, Bohan Zhang +6
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such…
Improving Protein Sequence Design through Designability Preference Optimization
Fanglei Xue, Andrew Kubaney, Zhichun Guo +4
Protein sequence design methods have demonstrated strong performance in sequence generation for de novo protein design. However, as the training objective was sequence recovery, it…
YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training
Yi Li, Zhichun Guo, Guanpeng Li +1
Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducin…