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这篇论文对持续学习场景下视觉-语言模型的环境感知OOD检测进行了理论分析，提出交叉环境最大概念匹配（MCM）分数，并证明了在环境转移为正且类匹配间隔有界条件下其假阳性率不劣于标准MCM，同时区分了可检测的软OOD与不可检测的硬OOD环境。
该论文提出将系统的本体状态定义为在给定查询空间上的完整响应谱，研究通过预测学习到的状态能在多大程度上恢复真实状态，并在合成系统与神经记录中给出预测-恢复的理论保证。
A theoretical study of denoising score matching showing how rank constraints and architectural bottlenecks in score networks affect generalization, deriving asymptotically precise training and test errors via truncated SVD in a random-feature model and identifying a natural rank threshold tied to the data subspace's orthogonal complement.
该论文将扩散模型、自回归及其混合体统一到一个“腐化格”（corruption lattice）框架中，证明调度的代价等于其与精确采样的散度，并指出零成本调度的最少步数由数据几何（如图的 treedepth）决定，还提出可在解码前预测调度优劣的核估计方法，并在 dLLM、图像与视频生成上验证了预测。
A new paper, 'Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation Learning', introduces MA-BC, which pools expert demonstrations only where observed actions do not disagree, and provides upper and lower bounds on sample complexity for learning from experts with differing objectives.
This monograph develops a unified account of training and inference dynamics in Power Law Decoder Representation language models (PLDR-LLMs), covering exact identities, renormalization, and experimental findings.
This paper formulates speculative decoding in Mixture-of-Experts models as a Stochastic Shortest Path problem and uses a diagnostic Oracle to demonstrate that optimal decisions follow a necessary condition balancing cost and progress.
This paper presents prior-free algorithms for improving multi-armed bandits, achieving optimal competitive ratios where scale, curvature, and horizon are free without noise, but not jointly under noise.
The paper establishes a theoretical framework showing that data and memory resources in autoregressive prediction are governed by a single predictive-energy spectrum, providing a joint scaling law.
This paper presents theoretical guarantees for one-shot magnitude pruning and compute-adaptive early exit in neural networks, analyzing compute-accuracy tradeoffs and deriving scaling laws through asymptotic and deep network models.
This paper studies the expressivity of spiking neural networks using polyhedral geometry, demonstrating that they can generate richer input space partitions than conventional feedforward ReLU networks.
This paper proves that unbounded gaps between update maps can coexist with vanishing predictive KL divergence in stationary symmetric Gaussian HMMs, showing that internal computational mismatches do not necessarily imply predictive failure.
The paper examines why pretrained language models fail to preserve continuous mixtures of token embeddings in reasoning, identifying architectural distortions and dynamical system effects as primary failure sources.
The paper analyzes the gap between teacher mimicry and true task performance in knowledge distillation under teacher misspecification using order-parameter methods, showing that mimicry metrics can be invariant while true errors increase with mismatch.
The paper shows that the Bernstein CVaR-UCBVI algorithm achieves a near-minimax leading-order regret bound for CVaR reinforcement learning without continuity assumptions on return laws.
This paper provides a theoretical analysis of the edge-of-stability phenomenon for the Adam optimizer on a one-dimensional quadratic function, proving that Adam exhibits a restoring mechanism that pushes sharpness toward a stability threshold.
This paper demonstrates that monotone adversarial corruptions can make certain multiclass and partial binary classification problems unlearnable, providing tight bounds on corruption budgets and extending previous results on binary classification.
This paper shows that sequence-pooled normalization in convolutional networks provides global context beyond the receptive field, supplying most of the context needed for sequence labeling and affecting attribution in network ablation studies.
This paper proves that there is no theoretical curse of multilinguality for embedding space structure, showing that the minimum dimensionality required grows only logarithmically with the number of languages, suggesting empirical issues stem from data and training conditions.
This paper provides a theoretical explanation for why the optimal sampling temperature for pass@k increases with the budget, deriving sufficient conditions and analyzing the empirical pattern without model training or queries.