most citedFrom AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

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cs.LG2026

FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning

Zhihan Yang, Jiaqi Wei, Xiang Zhang +6

Tabular data high-stakes critical decision-making in domains such as finance, healthcare, and scientific discovery. Yet, learning effectively from tabular data in few-shot settings…

cs.LG2025

TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis

Haokun Zhao, Xiang Zhang, Jiaqi Wei +4

Time series forecasting is central to decision-making in domains as diverse as energy, finance, climate, and public health. In practice, forecasters face thousands of short, noisy…

cs.LG2025

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation

Xiang Zhang, Jiaqi Wei, Zijie Qiu +5

Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks such as de novo peptide sequencing and protein modeling by their unidirectional natu…

cs.LG2025★ 5 cited

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

Jiaqi Wei, Yuejin Yang, Xiang Zhang +24

Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pi…

cs.LG2025

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing

Zijie Qiu, Jiaqi Wei, Xiang Zhang +6

De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrome…