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 237
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings.IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence
Regret matching (and its variants) and counterfactual regret minimization are central algorithms in practice for normal-form and extensive-form game solving.Discrete Diffusion Models are Minimax Optimal Under Sparsity
Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than…How Far Can Coding Agents Go in Visual Generation?
Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters.Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability.How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts
Low-rank adaptation (LoRA) applies every rank-one component of its update to every token, and mixture-of-experts variants replace this with a learned router that activates a fixed number of…Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
Pluralistic alignment needs to accommodate diverse preferences under limited human feedback.Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources
We study online task assignment with reusable resources.Tracing Morphology, Semantics, and Divergence in Language Acquisition
Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve into structured linguistic capabilities.Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models
Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling.Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT
Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization.What Information Buys in Active Inference
Information-theoretic exploration is of primary interest in methodological studies of reinforcement learning (RL) and computational cognitive neuroscience, while the established RL theory builds…Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams
Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences.Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history…Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models
Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them.How Inner Optimizers Shape Replica Scaling
Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of significant interest.Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved.Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Reliable uncertainty quantification remains an important challenge in AI.Backpropagating Verifier Rewards Through Continuous Flow Language Models
Reinforcement learning (RL) has substantially improved the reasoning of autoregressive and discrete-diffusion language models.Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization
Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically.