7 papers
DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs
Yi Li, Songtao Wei, Dongming Jiang +3
Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead.…
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…
Reinforcement Learning with LLM-Guided Action Spaces for Synthesizable Lead Optimization
Tao Li, Kaiyuan Hou, Tuan Vinh +3
Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible synthetic routes. Existing approach…
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…