AI & Machine Learning · Computer Science & AI
Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
Get the few papers worth your time in Machine Learning every morning.
Follow Machine Learning and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 24
Density-Aware Reward Scaling: A Reinforcement Learning Framework for Reward-Proportional Sampling
Reinforcement learning objectives that maximize expected return can concentrate probability mass on a small subset of high-reward outcomes, limiting diversity when multiple distinct solutions are…Accelerating Offloading-Based MoE Inference by Interleaving Concurrent Requests
Expert offloading enables large mixture-of-experts (MoE) language models to run locally, but conventional batching stalls the GPU while missing experts are fetched.Training Specialist LLMs on Your Phone: Forward-Only Optimization via Block-Spectral Coordinates
Large language models (LLMs) on personal devices keep user data local, but limited memory restricts their general capability.On the Complexity of Finite-Sum Nonconvex-Strongly-Concave Minimax Optimization with Exact Proximal Oracles
Finite-sum nonconvex-strongly-concave minimax problems arise in adversarial training and distributionally robust learning.Sleep-Disordered Breathing Detection with Quantum Recurrent Neural Networks
This work investigates the application of Quantum Recurrent Neural Networks to the binary classification of sleep-disordered breathing events in time-series data, aiming to distinguish between…Feeling of Knowing in Large Language Models
Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned.Anomaly Detection on Resource-Constrained Devices with Reservoir Computing
Anomaly detection at the edge requires the deployment of models that achieve competitive performance, while minimizing computational cost and memory footprint.Found by Search, Broken by a Shortcut: Transfer and Tampering of Searched Intrinsic Rewards
Can a search over innate drives find intrinsic rewards that help a reinforcement-learning agent learn, transfer to a task it never saw, and resist an exploit that was absent during the search?DLER: Dual-Logit Experience Replay for Online Continual Learning
Online continual learning aims to continuously train neural networks on a non-stationary data stream in a single pass.FourSight: Stable FP4 RL Post-Training via Sequence Filtering and Sparse Channel Compensation
Reinforcement learning (RL) post-training increasingly targets low-bit deployment, with NVFP4 weight-and activation quantization (W4A4) becoming the state-of-the-practice format.Fault-tolerant shallow recurrent decoders for sparse sensing with missing sensor measurements
Real-world sensing systems are inherently imperfect: sensors may fail, intermittently disconnect, operate with asynchronous sampling rates, and be subject to noise, leading to measurement streams…Attending to Memory Writes Reduces Seed Variability in Recurrent Length Generalization
Recurrent memory requires learning useful updates, but attention added to assist learning can also compromise length generalization.Cool Graphs: Active Property Search Towards Quantum Nano-Refrigerators
Nanodevices that absorb energy from their thermal environment can act as quantum refrigerators.MiCaDe: Online LiDAR-Camera Miscalibration Change Detection for Autonomous Driving
Reliable multi-sensor perception in autonomous vehicles requires accurate calibration between cameras and LiDAR.Modulating Merging Strengths via Joint Loss Estimation for LoRA-based Continual Learning
Continual learning (CL) aims to adapt models to new tasks while preserving previously learned knowledge.Deep Neural Operator Learning for stochastic heat equations and applications
In this paper, we establish a universal approximation theorem for solution operators of semilinear stochastic heat equations in spatial dimension \(d\ge2\), extending deep neural operator…ZEMA: confidence-calibrated signed descent with only one state
Recent work characterizes the Adam optimizer as sign descent modulated by gradient uncertainty: the update is the sign of the batch gradient, scaled by how strongly the batch supports that sign.Exact Selective Inference for Spectral Clustering via Piecewise-Constant Graph Perturbation
Cluster labels are often used twice: first to discover groups, and then to test whether the discovered groups differ.Auditing Multi-Proxy Explanations of Web User Preferences: Identification Boundaries and Abstention
Web systems infer user preferences from behavior, ratings, text, and LLM-generated profiles, yet explaining a proxy may reveal its measurement process rather than the preference of interest.Unifying and Beyond the Theory of Pervasive Capability Degradation in LLM Post-training SFT from Feature Interference
Pervasive Capability Degradation (PCD) refers to the phenomenon in which supervised fine-tuning (SFT), while adapting a large language model (LLM) to a target task, often erodes the general…