Paper-Conference

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
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MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
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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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Improving Time Series Forecasting via Instance-aware Post-hoc Revision
Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent …
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STaR: Towards Cognitive Table Reasoning via Slow-Thinking Large Language Models
Table reasoning with large language models (LLMs) plays a critical role in building intelligent systems capable of understanding and …
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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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