1 citations · 3 across the 22 of their papers we have counts for
26 papers
CastClaw: A Human-in-the-Loop Autonomous Agent for Interactive Time Series Forecasting
Xiaoyu Tao, Mingyue Cheng, Ze Guo +4
Practical time-series forecasting requires domain knowledge, constraints, plausibility checks, and human intervention. We present CastClaw, an interactive forecasting system center…
CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
Xiaoyu Tao, Mingyue Cheng, Bokai Pan +6
Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextu…
ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement
Bohou Zhang, Xiaoyu Tao, Mingyue Cheng +2
Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfuln…
TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning
Mingyue Cheng, Shuo Yu, Daoyu Wang +5
Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort and domain expertise. Recent l…
CastFlow: Bridging Evidence Acquisition and Accurate Prediction in Agentic Time Series Forecasting
Bokai Pan, Mingyue Cheng, Zhiding Liu +6
Time series forecasting is a fundamental task in temporal data analytics, and large language model (LLM)-based agents offer a promising paradigm through autonomous tool interaction…
GeoDecider: An Evidence-Grounded Agent for Geological Interpretation via Deliberative Reasoning
Xiaoyu Tao, Mingyue Cheng, Jiahao Wang +6
Geological interpretation infers subsurface properties and structures from indirect geophysical observations. Well-log classification provides a measurable setting by assigning geo…
Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting
Mingyue Cheng, Xiaoyu Tao, Qi Liu +2
Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While t…
TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning
Mingyue Cheng, Shuo Yu, Chuang Jiang +5
Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tabl…
Learning Agentic Time Series Forecasting via Trajectory Distillation and Memory-Based Reinforcement Learning
Xiaoyu Tao, Mingyue Cheng, Chuang Jiang +7
Recent agentic time series forecasting (TSF) approaches use large language models (LLMs) to autonomously orchestrate analytical and forecasting tools and reason about temporal patt…
AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
Xiaoyu Tao, Yuchong Wu, Mingyue Cheng +2
Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under comp…
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…
MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
Xiaoyu Tao, Mingyue Cheng, Ze Guo +4
Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)- based forecasters have made promising…
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
Mingyue Cheng, Xiaoyu Tao, Huajian Zhang +5
Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm…
Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent
Mingyue Cheng, Daoyu Wang, Qi Liu +7
Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although recent deep research agents (DRAs) achieve n…
OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
Tingyue Pan, Mingyue Cheng, Shilong Zhang +5
Cross-domain time series forecasting is a valuable task in various web applications. Despite its rapid advancement, achieving effective generalization across heterogeneous time ser…
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…
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Time Series Forecasting
Xiaohan Zhang, Tian Gao, Mingyue Cheng +5
Time series forecasting is typically treated as a single-pass numerical prediction task based on historical observations. However, real-world forecasting often requires expert-like…
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…
TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning
Chuang Jiang, Mingyue Cheng, Xiaoyu Tao +3
Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large language model (LLM)-based methods h…
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…
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…
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization
Yucong Luo, Mingyue Cheng, Jie Ouyang +2
Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch…
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…