USTC-AGI Research Group
USTC-AGI Research Group
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Mingyue Cheng
Associate Researcher
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China
兴趣爱好
LLMs and Agentic AI
Time Series Analysis
Recommender Systems
AI for Science
最新
From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement
Re^3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination
CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting
Fewer Battles, More Gain: An Information-Efficient Framework for Arena-based LLM Evaluation
GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation
Visual Autoregressive Modeling for Instruction-Guided Image Editing
StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning
A Comprehensive Survey of the LLM-Based Agent: The Contextual Cognition Perspective
BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation
From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation
Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
Position: Beyond Model-Centric Prediction — Agentic Time Series Forecasting
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization
Hierarchical multimodal llms with semantic space alignment for enhanced time series classification
Global Structure-aware and Feature-augmented Graph Neural Network for Heterophilic Graphs
Improving Time Series Forecasting via Instance-aware Post-hoc Revision
STaR: Towards Cognitive Table Reasoning via Slow-Thinking Large Language Models
A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction
Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via LLMs
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting
Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce
PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature
MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation
Tag-augmented Dual-target Cross-domain Recommendation
Preference Trajectory Modeling via Flow Matching for Sequential Recommendation
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
am-ELO: A Stable Framework for Arena-based LLM Evaluation
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation
TimeDART: A Diffusion Autoregressive Transformer for Self-supervised Time Series Representation
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
A Comprehensive Survey of Time Series Forecasting: Concepts, Challenges, and Future Directions
A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling
Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information
Revisiting the Solution of Meta KDD Cup 2024: CRAG
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Empowering Sequential Recommender Systems from Mixture of Collaborative Signals and Semantic Relatedness
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Unlocking the Potential of Large Language Models for Explainable Recommendations
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Advancing Time Series Classification with Multimodal Language Modeling
Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model
Exploring Adapter-based Transfer Learning for Recommender Systems
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
A General Tail Item Representation Enhancement Framework for Sequential Recommender Systems
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification
One Person, One Model - Learning Compound Router for Sequential Recommendation
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Nested Named Entity Recognition from Medical Texts: A Multi-task Learning Approach
Collaborative List-and-Pairwise Filtering from Implicit Feedback
Towards Automatic Designing of Deep Hybrid Network Architecture for Sequential Recommendation
Learning Transferrable User Representations with Sequential Behaviors via Contrastive Pre-training
LawyerPAN: A Proficiency Assessment Network for Trial Lawyers
Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement
NeurJudge: A Circumstance-aware Neural Framework for Legal Judgment Prediction
Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering
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