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- 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
- Hierarchical multimodal llms with semantic space alignment for enhanced time series classification
- STaR: Towards Cognitive Table Reasoning via Slow-Thinking Large Language Models
- Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
- Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
- HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation
- Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
- A Survey on Knowledge-Oriented Retrieval-Augmented Generation
- 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
- Learning Recommender Systems with Soft Target: A Decoupled Perspective
- Unlocking the Potential of Large Language Models for Explainable Recommendations
- Advancing Time Series Classification with Multimodal Language Modeling
- Generative Pretrained Hierarchical Transformer for Time Series Forecasting
- FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification