2 citations · 8 across the 12 of their papers we have counts for
12 papers
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…
SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
Shuaike Shen, Wenduo Cheng, Mingqian Ma +3
Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remain…
MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization
Zhuo Yang, Yeyun Chen, Jiaqing Xie +7
Molecular editing and optimization are multi-step problems that require iteratively improving properties while keeping molecules chemically valid and structurally similar. We frame…
Probabilistic Modeling of Multi-rater Medical Image Segmentation for Diversity and Personalization
Ke Liu, Shangde Gao, Yichao Fu +3
Lesion segmentation is inherently influenced by imaging uncertainty, arising from ill-defined lesion boundaries and inter-observer variability in diagnosis. To address this challen…
RSeg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection
Shuaike Shen, Ke Liu, Jiaqing Xie +5
Foundation models for medical image segmentation struggle under out-of-distribution (OOD) shifts, often producing fragmented false positives on OOD tumors. We introduce RSeg,…
Fine-tuning Flow Matching Generative Models with Intermediate Feedback
Jiajun Fan, Chaoran Cheng, Shuaike Shen +2
Flow-based generative models have shown remarkable success in text-to-image generation, yet fine-tuning them with intermediate feedback remains challenging, especially for continuo…
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Zhuo Yang, Jiaqing Xie, Shuaike Shen +13
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce S…
SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning
Shuaike Shen, Jiaqing Xie, Zhuo Yang +6
Large language models have emerged as transformative tools in molecular science, demonstrating remarkable potential in molecular property prediction and de novo molecular design. H…
From Sentences to Sequences: Rethinking Languages in Biological System
Ke Liu, Shuaike Shen, Hao Chen
The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-…
Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization
Jiajun Fan, Shuaike Shen, Chaoran Cheng +3
Recent advancements in reinforcement learning (RL) have achieved great success in fine-tuning diffusion-based generative models. However, fine-tuning continuous flow-based generati…
Floating Anchor Diffusion Model for Multi-motif Scaffolding
Ke Liu, Weian Mao, Shuaike Shen +4
Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzym…
De novo protein design using geometric vector field networks
Weian Mao, Muzhi Zhu, Zheng Sun +4
Innovations like protein diffusion have enabled significant progress in de novo protein design, which is a vital topic in life science. These methods typically depend on protein st…