大模型智能体常被幻觉困扰，这篇论文用渐进泛化边界加“看门狗”监控，理论实践两手抓，值得AI从业者深读。
arXiv:2507.15903v2 Announce Type: replace-cross Abstract: Empowered by large language models (LLMs), intelligent agents have become a popular paradigm…
提出“无窥视调优”方法，为大模型后训练提供可证明的泛化界限与鲁棒性保障。
arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalab…
将形式化验证与PAC-Bayesian理论结合，为过程奖励模型提供可证实的泛化边界，是AI安全领域的硬核进展。
arXiv:2606.20740v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level verification for Large Language Model (LLM) reasonin…
用Koopman算子理论推导多任务深度学习的泛化边界，为理论分析提供新视角。
arXiv:2512.19199v2 Announce Type: replace-cross Abstract: The paper establishes generalization bounds for multitask deep neural networks using operato…