activity
20242026
collaborators

44 papers

cs.LG2026

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…

cs.IR2026

ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

Tingyue Pan, Mingyue Cheng, Daoyu Wang +4

Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration. H…

cs.CL2026

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…

cs.LG2026

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…

cs.CL2026

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…

cs.LG2026

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…