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arXiv:2603.00683v1 Announce Type: new Abstract: State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms. However, the quadratic complexity of self-attention …
arXiv:2603.00686v1 Announce Type: new Abstract: Large Language Models have evolved from single-round generators into long-horizon agents, capable of complex text synthesis scenarios. However, current evaluation …
arXiv:2603.00696v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose …
arXiv:2603.00718v1 Announce Type: new Abstract: Real-world tool-using agents operate over long-horizon workflows with recurring structure and diverse demands, where effective behavior requires not only invoking …
arXiv:2603.00724v1 Announce Type: new Abstract: Large language model alignment via reinforcement learning depends critically on reward function quality. However, static, domain-specific reward models are often …
arXiv:2603.00725v1 Announce Type: new Abstract: Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on …
arXiv:2603.00823v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is …
arXiv:2603.00829v1 Announce Type: new Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms. A central challenge is detecting …
arXiv:2603.00840v1 Announce Type: new Abstract: Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist …
arXiv:2603.00842v1 Announce Type: new Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing …
arXiv:2603.00889v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforcement learning (RL)-based …
arXiv:2603.00907v1 Announce Type: new Abstract: The growing computational and memory demands of the Key-Value (KV) cache significantly limit the ability of Large Language Models (LLMs). …