1 citations · 1 across the 3 of their papers we have counts for
5 papers
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery
Yikun Zhang, Xiwei Cheng, Tianyu Liu +2
Building state-of-the-art (SOTA) predictive models for drug discovery requires expensive search over tools, architectures, and training strategies. Current LLM-based agents can fin…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Graph Generative Pre-trained Transformer
Xiaohui Chen, Yinkai Wang, Jiaxing He +4
Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured da…
Large Language Model is Secretly a Protein Sequence Optimizer
Yinkai Wang, Jiaxing He, Yuanqi Du +5
We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. Directed evolution ha…