Paper-Conference

TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via LLMs
Large language models (LLMs) have shown promise in multivariate time series classification (MTSC). To effectively adapt LLMs for MTSC, …
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Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce
Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users’ complex search …
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MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation
The primary form of user-internet engagement is shifting from leveraging implicit feedback signals, such as browsing and clicks, to …
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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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am-ELO: A Stable Framework for Arena-based LLM Evaluation
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models …
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HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) has emerged as an effective approach for addressing the knowledge outdating problem in Large …
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TimeDART: A Diffusion Autoregressive Transformer for Self-supervised Time Series Representation
Self-supervised learning has garnered increasing attention in time series analysis for benefiting various downstream tasks and reducing …
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Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
With the widespread application of multimodal large language models in scientific intelligence, there is an urgent need for more …
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ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
Designing effective models for learning time series representations is foundational for time series analysis. Many previous works …
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Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Pre-training universal models across multiple domains to enhance downstream tasks is a prevalent learning paradigm. However, there has …
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