把个性化奖励拆解成可解释概率偏好，不确定性建模让LLM对齐更稳更准。
arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared …
非线性状态空间模型遇上递归熵变分推断，新方法突破复杂概率推理难题。
arXiv:2511.15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space model…
持续学习如何对抗遗忘？贝叶斯方法综述给你严谨答案，从核心原理到前沿进展一网打尽。
arXiv:2507.08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge f…
提出结构化非参数变分推断新方法，有效建模依赖潜在变量，突破传统变分推断局限。
arXiv:2606.15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian lea…
推出变分推测解码，从单路径优化转向序列接受，革新大模型推理加速
arXiv:2602.05774v4 Announce Type: replace Abstract: Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persis…