3 citations · 4 across the 25 of their papers we have counts for
28 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…
Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Sample-efficient reinforcement learning for autonomous driving is often limited by the trade-off between data efficiency and model bias. While world models reduce the reliance on c…
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…
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…
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…
AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions
Daoyu Wang, Mingyue Cheng, Qingchuan Li +4
Training LLMs to act as capable agents requires learning from multi-turn interactions with environments. However, multi-turn interaction trajectories generated by heterogeneous age…
SocraticPO: Policy Optimization via Interactive Guidance
Zirui Liu, Tingyue Pan, Jie Ouyang +8
Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewards provide an optimization dir…
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…
GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation
Mingyue Cheng, Yitong Zhou, Jiahao Wang +2
Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequences. Despite recent progre…
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…
Kinematics-Aware Latent World Models for Data-Efficient Autonomous Driving
Jiazhuo Li, Linjiang Cao, Qi Liu +1
Data-efficient learning remains a central challenge in autonomous driving due to the high cost and safety risks of large-scale real-world interaction. Although world-model-based re…
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…
PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization
Tingyue Pan, Jie Ouyang, Mingyue Cheng +7
Academic paper search is a fundamental task in scientific research, yet most existing approaches organize retrieval around predefined workflows or structured interaction protocols…
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…
Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
Jintao Zhang, Mingyue Cheng, Zirui Liu +3
Time series generation is critical for a wide range of applications, which greatly supports downstream analytical and decision-making tasks. However, the inherent temporal heteroge…
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…
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…
Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval
Jiawei Cao, Jie Ouyang, Zhaomeng Zhou +4
Temporal Information Retrieval (TIR) is a critical yet unresolved task for modern search systems, retrieving documents that not only satisfy a query's information need but also adh…
Visual Autoregressive Modeling for Instruction-Guided Image Editing
Qingyang Mao, Qi Cai, Yehao Li +5
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the ed…