3 citations · 6 across the 18 of their papers we have counts for
7 papers · 1 filter
Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning
Daoyu Wang, Mingyue Cheng, Qingchuan Li +3
Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative appl…
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
Mingyue Cheng, Yaguo Liu, Daoyu Wang +2
Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dy…
From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
Xiaoyu Tao, Shilong Zhang, Mingyue Cheng +5
Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent adv…
Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
Yitong Zhou, Yucong Luo, Mingyue Cheng +4
To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning archi…
Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
Mingyue Cheng, Jiahao Wang, Daoyu Wang +3
Time series forecasting (TSF) is a fundamental and widely studied task, spanning methods from classical statistical approaches to modern deep learning and multimodal language model…
Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
Jintao Zhang, Mingyue Cheng, Xiaoyu Tao +2
Time series forecasting models are becoming increasingly prevalent due to their critical role in decision-making across various domains. However, most existing approaches represent…