Academic
arXiv:2602.22650v1 Announce Type: new Abstract: In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist …
arXiv:2602.22680v1 Announce Type: new Abstract: Large language models have enabled agents that reason, plan, and interact with tools and environments to accomplish complex tasks. As …
arXiv:2602.22702v1 Announce Type: new Abstract: Existing neural network calibration methods often treat calibration as a static, post-hoc optimization task. However, this neglects the dynamic and …
arXiv:2602.22718v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has been widely applied to Large Language Model (LLM) post-training to align model outputs …
arXiv:2602.22743v1 Announce Type: new Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges …
arXiv:2602.22751v1 Announce Type: new Abstract: Large reasoning models (LRMs) have emerged as a powerful paradigm for solving complex real-world tasks. In practice, these models are …
arXiv:2602.22758v1 Announce Type: new Abstract: We decompose physician disagreement in the HealthBench medical AI evaluation dataset to understand where variance resides and what observable features …
arXiv:2602.22769v1 Announce Type: new Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical for …
arXiv:2602.22771v1 Announce Type: new Abstract: Clinical decisions are often required under incomplete information. Clinical experts must identify whether available information is sufficient for judgment, as …
arXiv:2602.22808v1 Announce Type: new Abstract: Despite the remarkable progress of large language models (LLMs), the capabilities of standalone LLMs have begun to plateau when tackling …
arXiv:2602.22814v1 Announce Type: new Abstract: Agentic AI increasingly intervenes proactively by inferring users' situations from contextual data yet often fails for lack of principled judgment …
arXiv:2602.22822v1 Announce Type: new Abstract: The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material …
