Paper-Conference

Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
Time series forecasting (TSF) traditionally relies on fast-thinking paradigms that map historical observations directly to future …
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TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning
Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although …
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Hierarchical multimodal llms with semantic space alignment for enhanced time series classification
Time series classification plays a fundamental role in a wide range of real-world applications. Recently, large language models (LLMs) …
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A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction
Intraoperative hypotension (IOH) prediction using past physiological signals is crucial, as IOH may lead to inadequate organ perfusion …
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Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting
Time series forecasting models are becoming increasingly prevalent due to their critical role in decision-making across various …
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Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. …
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Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user …
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Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large …
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TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
Large language models (LLMs) have shown promise in multivariate time series classification (MTSC). To effectively adapt LLMs for MTSC, …
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Tag-augmented Dual-target Cross-domain Recommendation
Cross-domain recommendation (CDR) has been proposed to alleviate the data sparsity issue in recommendation systems and has garnered …
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