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arXiv:2603.03820v1 Announce Type: new Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, …
arXiv:2603.03830v1 Announce Type: new Abstract: Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. …
arXiv:2603.03865v1 Announce Type: new Abstract: While federated learning protects data privacy, it also makes the model update process vulnerable to long-term stealthy perturbations. Existing studies …
arXiv:2603.03867v1 Announce Type: new Abstract: Link prediction (LP) plays a central role in graph-based applications, particularly in social recommendation. However, real-world graphs often reflect structural …
arXiv:2603.03872v1 Announce Type: new Abstract: Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating …
arXiv:2603.02676v1 Announce Type: new Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that …
arXiv:2603.02684v1 Announce Type: new Abstract: Subtle and indirect hate speech remains an underexplored challenge in online safety research, particularly when harmful intent is embedded within …
arXiv:2603.02701v1 Announce Type: new Abstract: Optimizing communication topology is fundamental to the efficiency and effectiveness of Large Language Model (LLM)-based Multi-Agent Systems (MAS). While recent …
arXiv:2603.02709v1 Announce Type: new Abstract: We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product …
arXiv:2603.02760v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability, and parallelism. However, …
arXiv:2603.02775v1 Announce Type: new Abstract: Large Language Models (LLMs) show significant potential in AI mathematical tutoring, yet current evaluations often rely on simplistic metrics or …
arXiv:2603.02789v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) enhance the potential of natural language processing. However, their actual impact on document information extraction …