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 198
Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost.Behavioral Compositionality of Sparse Autoencoder Representations
Sparse autoencoders (SAEs) represent model activations with sparse, linearly decoded features, but the downstream effects of those features need not compose additively.Rethinking Learning from Label Proportions via Moment Matching
Learning from label proportions (LLP) is a weakly supervised setting where training data are grouped into bags with only bag-level label proportions observed, and the goal is to learn a classifier…Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means
We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has applications to quantum state tomography, the…TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processing and…Can Capability Enhancement be Localized?
Transformer-based language models have shown strong capabilities across many tasks and domains.Personalized Projected Ascent for Federated Stackelberg Optimization
Federated minimax problems, such as distributionally robust optimization and adversarial training, include an inner maximization variable built from each client's local data.PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning
Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text rather than as temporal evidence.Spectrum Is Not Enough: A Random-Matrix Account of Data-Free Preconditioning for Differentially Private Learning
Data-free preconditioning builds the Kronecker factors of KFAC from synthetic probes instead of private data.Semantic Latent Alignment for Flow Matching with Invertible Bi-Lipschitz Adapters
Pretrained latent spaces trade compression, reconstruction, and the learnability of the coordinates a downstream generator has to work in, which complicates adapting these spaces to different…When Should a Low-Light Pipeline Generate? Auditable Risk Control for Selective Generative Enhancement
Generative low-light enhancers recover plausible texture but may hallucinate structure; restoration networks preserve structure but can leave regions under-enhanced, and neither advantage is…Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning
We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning.Does On-Policy Distillation Need to Complete Every Rollout?
On-policy distillation (OPD) transfers teacher knowledge to a student through feedback on student-generated rollouts, but generating complete rollouts and computing per-token teacher feedback are…Dimension-Free PAC-Bayes Generalization for Deep Neural Networks via Dirichlet Processes
Classical PAC-Bayesian generalization theory places prior distributions over Euclidean parameter space $Θ\subseteq \mathbb{R}^D$, causing relative entropy to scale with network dimension $D$.Online Quantile Omniprediction for Proper Losses
The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omniprediction.Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options
To enable well-informed customer choices and redistribute demand across service options, providers can display probabilities regarding predicted service levels at the time of booking, effectively…L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data
Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts.Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory
Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them.Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation
Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feeding each prediction back as input for the next…Memorilla: Latent Semantic Memory for LLMs
Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier actions.