13 papers
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced 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…
Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
Mingyue Cheng, Shuo Yu, Daoyu Wang +7
Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agents take on more complex reasonin…
STaR: Towards Effective and Stable Table Reasoning via Slow-Thinking Large Language Models
Huajian Zhang, Mingyue Cheng, Yucong Luo +1
Table reasoning with large language models (LLMs) plays a critical role in building intelligent systems capable of understanding and analyzing tabular data. Despite recent progress…
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng +3
Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) have demonstrated strong generalization and r…
BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation
Yupeng Li, Mingyue Cheng, Yucong Luo +3
Multi-behavior sequential recommendation aims to capture users' dynamic interests by modeling diverse types of user interactions over time. Although several studies have explored t…
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
Yitong Zhou, Mingyue Cheng, Qingyang Mao +7
With the widespread application of multimodal large language models in scientific intelligence, there is an urgent need for more challenging evaluation benchmarks to assess their a…