6 papers
MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
Haoyu Dong, Rui Sheng, Shuhao Zhang +7
Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as effi…
Exploiting Multicast for Accelerating Collective Communication
Chao Xu, Xu Zhang, Zihang Luo +5
Reducing collective communication latency is a critical goal for large model training and inference in both academia and industry. Many-to-many communications, such as AllGather an…
CLORE: Content-Level Optimization for Reasoning Efficiency
Yuyang Wu, Qiyao Xue, Guanxing Lu +4
Reinforcement learning post-training has improved the reasoning ability of large language models, but often produces unnecessarily long, repetitive, or semantically opaque reasonin…
Can Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning
Yuyang Wu, Yue Huang, Shuaike Shen +8
Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous evaluation of scientific to…
VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
Yurui Xiang, Xingyi Mao, Rui Sheng +7
Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and imperfect reasoning reliabil…
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
Yuyang Wu, Jinhui Ye, Shuhao Zhang +3
Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify pote…