3 citations · 6 across the 18 of their papers we have counts for
8 papers · 1 filter
From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation
Qingchuan Li, Mingyue Cheng, Zirui Liu +3
Logical reasoning is a core challenge in natural language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical the…
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…
PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature
Daoyu Wang, Mingyue Cheng, Shuo Yu +4
Understanding and reasoning on the large-scale scientific literature is a crucial touchstone for large language model (LLM) based agents. However, existing works are mainly restric…
MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation
Shuo Yu, Mingyue Cheng, Daoyu Wang +4
The primary form of user-internet engagement is shifting from leveraging implicit feedback signals, such as browsing and clicks, to harnessing the rich explicit feedback provided b…
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…
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
Mingyue Cheng, Yucong Luo, Jie Ouyang +9
Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-…