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arXiv:2603.03309v1 Announce Type: new Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items …
arXiv:2603.03310v1 Announce Type: new Abstract: Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear …
arXiv:2603.03311v1 Announce Type: new Abstract: This paper documents the architecture, development practices, and preserved artifacts of the Logovista English--Japanese machine translation system, a large, explicitly …
arXiv:2603.03317v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) allow agents to execute complex natural language tasks. Many LLM applications, such as …
arXiv:2603.03328v1 Announce Type: new Abstract: Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language …
arXiv:2603.03329v1 Announce Type: new Abstract: Despite significant strides in language models in the last few years, when used as agents, such models often try to …
arXiv:2603.03330v1 Announce Type: new Abstract: Large language models (LLMs) often present answers with high apparent confidence despite lacking an explicit mechanism for reasoning about certainty …
arXiv:2603.03331v1 Announce Type: new Abstract: Photoplethysmography (PPG) is a widely used non-invasive sensing modality for continuous cardiovascular and physiological monitoring across clinical, laboratory, and wearable …
arXiv:2603.03332v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness …
arXiv:2603.03333v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by proposing tokens with a lightweight draft model and selectively accepting them using …
arXiv:2603.03334v1 Announce Type: new Abstract: The evaluation of Large Language Models (LLMs) on mathematical reasoning has largely focused on elementary problems, competition-style questions, or formal …
arXiv:2603.03335v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a wide range of capabilities, including mathematical reasoning, code generation, and linguistic behaviors. We show …