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论文提出一种对抗鲁棒的广义编码计算（GCC）扩展框架，通过平滑正则化限制恶意worker输出的影响，并给出随对抗worker比例衰减的非渐近误差界，适用于分布式系统中的近似编码计算。
This paper diagnoses systematic errors in attribution patching, a gradient-based approximation used for causal localization in language models, and proposes a second-order correction using Hessian-vector products that improves reliability with minimal additional computational cost.
This paper develops a sign-separated finite-time error analysis for constant step-size Q-learning, decomposing the error into negative and positive parts and providing bounds that reveal an asymmetry related to overestimation.
This paper analyzes the 'training in imagination' paradigm in model-based reinforcement learning, deriving optimal sample allocation strategies and characterizing how dynamics and reward model errors affect policy returns.