Paper-Conference

Fewer Battles, More Gain: An Information-Efficient Framework for Arena-based LLM Evaluation
Arena-based evaluation has become a key method for assessing large language models (LLMs) through head-to-head model comparisons, …
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Visual Autoregressive Modeling for Instruction-Guided Image Editing
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global …
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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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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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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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