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 167
LaMER: Language-Conditioned Multi-Expert Routing for Weakly Supervised Referring Expression Comprehension
Referring Expression Comprehension (REC) localizes the image region described by a natural language expression, underpinning applications such as autonomous navigation, human-robot interaction, and…A Simple and General Approach to top-$k$ Selection
This paper considers the Bayesian top-$k$ identification problem, wherein a decision maker is tasked with selecting $k$ items from a pool of items of unknown quality.Co-Evolving Policy and Reward for Code Generation: A Mechanistic Study of Single-Adapter Drift
Reinforcement learning with verifiable rewards (RLVR) is particularly well suited to code generation because generated programs can be objectively evaluated through execution.Local data augmentation for imbalanced binary classification
Facing imbalanced binary classification, we aim at maximizing the recall while limiting the proportion of false detections in the minority class.R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes
We develop R2-GP, an R2D2-type prior for Gaussian process (GP) generalised additive models (GAMs) that calibrates explained variance ($R^2$) for Matérn covariance kernels.Rethinking LLM Fine-Tuning via Weight Space Reparameterization: Preserving Safety during Downstream Adaptation
Fine-tuning large language models (LLMs) on downstream tasks often weakens their previously aligned safety behavior, creating an inherent trade-off between task adaptation and safety preservation.Regime-Aware Interpretable Ranking for Sector Rotation
Sector rotation is a common decision in benchmark-relative portfolio management, where portfolio managers rank sectors for overweight and underweight positions.Residual-Based Adaptive Refinement in Hybrid Variational-Collocation PINNs for Singular Elliptic PDEs with Mixed Dirichlet-Neumann Boundary Conditions
Solutions of second-order elliptic PDE problems on nonconvex or cracked domains with mixed Dirichlet-Neumann conditions typically lie outside $H^2(Ω)$.VeriCUA: Learning Proactive Verification for Reliable GUI Agent Rewards
Reinforcement learning for computer-use agents increasingly relies on proactive reward agents that interact with the post-execution GUI environment to verify what recorded screenshots cannot reveal.On Learning Expand-and-Sparsify Representations
Expand-and-sparsify ($\texttt{EaS}$) representations lift an input to a much higher dimension through a linear map and keep only the $k$ largest coordinates.A Simple and Unified Method for Gradient-Variation Regret
Gradient-variation online learning has attracted increasing attention for its connections to game theory and optimization.Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting
Generative models for precipitation nowcasting are trained and evaluated predominantly on wet-biased datasets, which over-represent precipitation events to emphasize rare extremes.Strength Controls Change How Interventions Compare Across Sparse Autoencoder Codes
Changing sparse autoencoder (SAE) codes can alter both intervention direction and magnitude, so comparing intervention effects requires a strength control specifying what is held equal.Learn the Task, Not the Schedule: Reducing Spurious Cross-Arm Coupling in Bimanual Imitation Learning
A bimanual demonstration specifies both what the robot should do and one particular schedule for when each arm should act.RouterWorld: Scaling Conversational AI with Latency-Aware LLM Routing
Conversational AI systems built on Large Language Models (LLMs) require routing mechanisms that balance answer quality and response latency at scale.A Fisher-Geometric View of Multi-Teacher On-Policy Distillation
Consolidating specialized foundation models into a unified generalist is a central goal of post-training.A Semantic Knowledge Graph Construction Pipeline for Vehicle Intention Prediction in nuScenes
Knowledge graphs (KGs) are increasingly used in autonomous driving (AD) to make traffic scene entities and relations explicit for prediction and reasoning.Breaking Diffusion: Parallel Reasoning with Draft Refinement
Drafting an answer allows a reasoning model to reconsider each decision in the context of the others.Training Natural Schemers: RL Can Amplify Oversight-Conditional Behavior
One of the central concerns for the safety of advanced AI is scheming: when a model strategically complies under oversight but pursues its own goals when unobserved.SafeWall: A Risk-Aware Job Walltime Recommendation for HPC Scheduling
In high-performance computing (HPC), users must submit resource requirements for their jobs, with user-supplied walltime being a critical parameter.