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Newest papers, page 48
Quantizing Recurrent Language Models Is Easier Than You Think
Linear recurrences are becoming an increasingly common building block in large language models, replacing the growing KV cache of softmax attention with a fixed-size recurrent state that enables…The (Butter)fly Dream: Linear Separability of World-Model Rollouts in a Connectome-Constrained Visual Model
Zhuangzi dreamt he was a butterfly and, on waking, could not tell which was the dream.A Self-Generated Floor on Adam's $\varepsilon$: Evidence from Power-Law Matrix Sensing
In nonconvex Burer--Monteiro low-rank matrix sensing of $U^\ast \in \mathbb{R}^{V \times D}$ under power-law covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$, the precision-wall approximation predicts…Efficient Lempel-Ziv Prediction with the Run-Length BWT
Lempel-Ziv Plurality (LZP) is an algorithm of Kosoy that predicts the next symbol of a string by using its Lempel-Ziv factorization, and makes at most $O(z\log n)$ mistakes on every string of length…Robust Remote ECG Monitoring under Energy Constraints: Anomaly Filtering versus Predictive Uncertainty
Machine learning for remote electrocardiogram monitoring must handle noise and diseases absent from training under tight resource constraints.Machine Learning Beyond Statistical Consistency for Hybrid AI
We propose a new definition of machine learning, according to which a machine learns a function when it computes the function successfully, that is, when the value it returns for each input has a…Watch Your Step: Adam's Margin Maximization Bias Depends on the Learning Rate Schedule
We study the implicit bias of Adam and its similarity to sign gradient descent on homogeneous models in binary classification tasks.ReWY: Faster Backpropagation for Delta-Rule Attention - From 7 to 3 Matrix Multiplications
Delta-rule linear attention underlies language models such as Kimi Linear and Qwen3-Next.Direct Knowledge Transfer for Offline Q-Learning
Data-driven sequential decision making in marketing and healthcare can benefit from observations collected in related environments, particularly when data in the environment of interest are limited.OC-OPD: Observation-coordinated Supervision for On-Policy Distillation
Reverse and forward Kullback--Leibler (KL) divergences provide complementary supervision in on-policy distillation: reverse KL refines the student's active token rankings, and forward KL allocates…Large-Scale Bayesian Optimisation Using Uncertainty-Calibrated Product-of-Experts Gaussian Process Models with Trust Regions
The use of collections of independent local Gaussian processes (GP-ind) combined with trust regions is a widely adopted approach for large-scale and high-dimensional Bayesian optimisation (BO).Seeing Why: Exact Evidence Maps for Quantum and Optical Image Classifiers
When an image classifier decides, we can usually only estimate why.MIRA: Mutual Information guided calibration for Reliable Test-Time Adaptation
Test-time adaptation (TTA) updates a deployed model using only unlabeled target data, but standard entropy-based methods often rely on deterministic confidence and can reinforce overconfident errors…Uncertainty-Aware 3D Position Reconstruction in CZT Drift-Strip Detectors
Accurate three-dimensional interaction-position reconstruction is important for CZT drift-strip detectors, but conventional methods can suffer from degraded performance in challenging detector…SensOFormer: Foundation Models for Particle Accelerators
Large-scale scientific facilities, such as particle accelerators, are complex systems composed of thousands of interacting components that must operate collectively to support diagnostics and…Scalar Smoothness Is One Point on a Besov Spectrum
Studies of neural network generalization and robustness often summarize smoothness with a single number, such as a Lipschitz constant or a Hessian curvature statistic.ECGJev: Question-Conditioned Option Scoring for 12-Lead ECG Interpretation
Clinical interpretation of a 12-lead electrocardiogram (ECG) often requires answering targeted diagnostic questions with clear, quantifiable decisions.METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis
Short cognitive events contain unequal and often sparse transition evidence, so raw EEG microstate distances can reflect finite sampling rather than neural reconfiguration.Beliefs in feedback: Factored representations of interacting agents in transformers
Complex systems, from ecosystems to brains, are usually modeled as modular components coupled through feedback rather than as indivisible wholes.Reinforcement Learning in High-Energy Physics: A Survey from Accelerator Control to Theory Exploration
Reinforcement learning (RL) is a machine learning paradigm in which an agent learns a decision-making policy by interacting with an environment, guided by a scalar reward rather than explicit…