48 citations · 76 across the 12 of their papers we have counts for
12 papers
CTTS: Collective Test-Time Scaling
Zhende Song, Shengji Tang, Peng Ye +4
Test-time scaling (TTS) has emerged as a promising, training-free approach for enhancing large language model (LLM) performance. However, the efficacy of existing methods, such as…
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li +117
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…
A Self-Evolving AI Agent System for Climate Science
Zijie Guo, Jiong Wang, Fenghua Ling +19
Scientific progress in Earth science depends on integrating data across the planet's interconnected spheres. However, the accelerating volume and fragmentation of multi-sphere know…
Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System
Wanghan Xu, Wenlong Zhang, Fenghua Ling +8
Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. This approach not only mitigates limita…
SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model
Xinghao Wang, Feng Liu, Rui Su +5
Recent advances in deep learning have revolutionized seismic monitoring, yet developing a foundation model that performs well across multiple complex tasks remains challenging, par…
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning
Chuming Li, Ruonan Jia, Jie Liu +5
Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost…