8 citations · 20 across the 19 of their papers we have counts for
6 papers · 1 filter
GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory
Xiaoyun Yu, Xiangfei Qiu, Yonggui Huang +7
Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further improves their performance by…
A General Harness for Protein Foundation Model Fitness Prediction
Yang Tan, Qijia Tian, Gangyu Sun +5
Accurate fitness prediction is central to protein engineering and understanding sequence-function relationships. With advances in deep learning, protein foundation models (PFMs) ha…
ViroBench: Benchmarking Nucleotide Foundation Models on Viral Genomics Tasks
Dongxin Ye, Fang Hu, Han Hu +6
Nucleotide sequences constitute the fundamental genetic basis of biological systems, rendering viral genomic analysis critical for biomedical advancement. Despite progress in biolo…
Self-evolving AI agents for protein discovery and directed evolution
Yang Tan, Lingrong Zhang, Mingchen Li +5
Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFacto…
MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models
Xiaoyun Yu, Li fan, Xiangfei Qiu +7
While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in re…
Rank-and-Reason: Multi-Agent Collaboration Accelerates Zero-Shot Protein Mutation Prediction
Yang Tan, Yuanxi Yu, Can Wu +7
Zero-shot mutation prediction is vital for low-resource protein engineering, yet existing protein language models (PLMs) often yield statistically confident results that ignore fun…