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 87
Learning from Points and Patches: A Pre-trained Hybrid Attention Transformer for Time Series Analysis
Time-series data generated by Web services and connected systems support applications such as user activity analysis, service monitoring, and resource management.Less is more: learning what to profile for GPU kernel optimization
Large language models have recently shown strong potential for automatically optimizing GPU kernels, yet their effectiveness depends critically on the hardware information available to the kernel…MM-Net: A Multimodal Multi-Task Network for Sleep Staging and Sleep Apnea Assessment After Ischemic Stroke
Automated polysomnography after ischemic stroke is challenging because of altered sleep-related neural activity and concurrent respiratory disturbances.Recall or Relearn? A Mixture of In-Context Experts for Recurring Concept Drift
Tabular foundation models (TFMs) are being applied to Web data streams such as network intrusion detection, where they adapt by updating the labelled rows of their context without retraining.Efficient Quantization Training with Stochastic Rounding via Structured Subspace Randomness
Low-precision training often relies on stochastic rounding (SR) to keep quantized gradients unbiased and prevent small updates from rounding to zero.Normalized Cuts and Data Clustering via Matrix Fractional Programming
The normalized cut (NCut) problem is a fundamental one in the area of data clustering.Balanced State Subspaces for Mamba-2 State Compression
Mamba-2 compresses all context into a fixed-size recurrent state that dominates decoding memory and bandwidth.Dream-COP: Guiding Algorithm Discovery for Combinatorial Optimization Problems in an Experience World
Large language models (LLMs) enable algorithm discovery for combinatorial optimization problems (COPs) through iterative program generation and evaluation.God Does Not Play Dice: Enforcing Batch Invariance in Agentic Kernel Generation
Large language model (LLM) serving systems dynamically batch user requests for GPU execution, so the same request may be processed alongside different requests across runs.The Batch Size Can Be One: Test-Time Adaptation from Classifier Geometry
Test-time adaptation updates a deployed model on an unlabeled test stream.Deep Bayesian Filtering for Nonlinear Structural System Identification: A Tutorial on Structural and Physical Inductive Biases
Nonlinear structural system identification remains challenging when governing physics is only partially known and measurements are sparse.When Does Perturbation Consistency Add Information Beyond Softmax Confidence? Theory and Evidence for the Decision Consistency Framework
Perturbation consistency scores judge whether a prediction survives small input changes.When Is a Cheap Fairness Edit Safe? Screening Post-Hoc Edits in Two-Sided Matching Markets
Two-sided matching systems, such as admissions pipelines, allocate opportunities jointly.TACO-Graph: Automated Construction of Text-Atributed Graphs
Graph-structured representations of text data have become foundational across machine learning, yet it remains unclear how to systematically construct a graph from a given dataset for a target task.Log-Derivative Trick for Parametric Markov Chains
Optimizing expected rewards and reachability probabilities in parametric Markov chains (pMCs) is central to the analysis and synthesis of probabilistic systems with loops.Impossibility of No-Regret in Multi-Agent Bidding
We consider $T$ repeated second-price auctions across $n$ pacing bidders with i.i.d. item arrivals, and show that it is computationally impossible for all bidders to achieve no-regret simultaneously…Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs
Diffusion vision-language models (DVLMs) process their entire input at every denoising step.Specialization, Diversity, and Scaling in Child-Scale Language Models
Modern language models are trained on far more language than children experience, but we know much less about learning from small, naturalistic datasets.From Biological Curves to Soft Modular Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement
Animals exhibit diverse shape-changing behaviors, from the undulatory swimming of eels to the arching movements of inchworms.Quantifying and Prioritizing Stealthy Source-Manipulation Threats Against Multi-Source Anomaly Detectors
Security pipelines often fuse several data sources and protect a downstream model with a cross-source anomaly detector.