37 citations · 47 across the 16 of their papers we have counts for
20 papers
SheetCompass: Hierarchical Relation Graphs for Agentic Spreadsheet Reasoning
Panjing He, Mingyue Cheng, Yucong Luo +2
Spreadsheets are widely used to organize, analyze, and manipulate semi-structured data, yet automated spreadsheet reasoning remains challenging for large language models (LLMs). Re…
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…
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…
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…
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…
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…
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-…
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation
Jie Ouyang, Tingyue Pan, Mingyue Cheng +4
While Retrieval-Augmented Generation (RAG) has emerged as an effective approach for addressing the knowledge outdating problem in Large Language Models (LLMs), it still faces a cri…
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…
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
Yucong Luo, Qitao Qin, Hao Zhang +4
Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more…
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…
Revisiting the Solution of Meta KDD Cup 2024: CRAG
Jie Ouyang, Yucong Luo, Mingyue Cheng +4
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Shuo Yu, Mingyue Cheng, Qi Liu +6
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of…
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation
Qitao Qin, Yucong Luo, Mingyue Cheng +2
Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial at…
Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng, Yiheng Chen, Qi Liu +2
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier mod…
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Zhiding Liu, Jiqian Yang, Mingyue Cheng +2
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Neverthel…
Unlocking the Potential of Large Language Models for Explainable Recommendations
Yucong Luo, Mingyue Cheng, Hao Zhang +3
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enha…
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
Mingyue Cheng, Qi Liu, Zhiding Liu +3
Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the…