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 118
When is early stopping beneficial under label noise? A phase diagram for kernel gradient flow
Early stopping is commonly used to prevent fitting label noise from degrading generalization.Ranking Is Not Deciding: Benchmark Shortcuts and Operating-Point Collapse in Query-Time RAG Poisoning Defenses
Query-time filters that screen each retrieved document before it reaches the generator are the dominant published defense against retrieval-augmented generation poisoning, and they report…Software Engineering LLMs: Why We Need Them and How Software Engineering Helps Build Them
Software engineering (SE) expertise can shape how coding large language models (LLMs) are trained and which engineering capabilities they acquire.Numina Fuse: Interactive Autoformalization in Lean 4
Numina Fuse is an agent-native application for formalizing mathematics in Lean 4.Stable Dimensionality Reduction via Isometric Centroid-Encoder
Many supervised dimensionality reduction methods promote intra-class compactness and inter-class separation but do not explicitly constrain class geometry, leading to variability in the relative…DIGEST: Discovery-Guided LLM Tuning for Symbolic Regression
Symbolic regression (SR) seeks to discover mathematical expressions directly from data, without requiring task-specific scientific descriptions.Intervention-Certified Amplification: A Validity Gate for $σ$-Weighted Post-Training
Hybrid post-training often mixes supervised fine-tuning (SFT) and reinforcement learning (RL), using training-side signals to set each sample's balance.Min-Marginal Power Indices for Multi-Objective Feature Selection
Game-theoretic feature selection scores each feature by its average marginal contribution across coalitions.Counterfactual Test-Time Augmentation for Shortcut Removal in Language Models
Large language models (LLMs) can exploit shortcuts, i.e., correlations present in training data that may not hold at test time.Rank-1 Weight-Corrected Trainable Weight Averaging for Out-of-Distribution Detection
Out-of-distribution (OOD) detection identifies inputs outside the training distribution.Real-Time Human Trajectory Prediction from RGB-D for Mobile Robots Using Bi-Mamba
Predicting the short-term future trajectory of a person from RGB-D cameras is a key capability for mobile robots navigating in dynamic and human populated environment.Learning Population Dynamics with Variational Doob-Wasserstein Flow
This paper introduces a novel simulation-free framework for learning unbalanced population dynamics on the Riemannian Wasserstein-Fisher-Rao (WFR) manifold, termed Variational Doob-Wasserstein Flow…Neural Eigenfluid Bases: Spectral Objectives, Gauge, and Topology
Reduced-order models simplify fluid simulation by representing complex flows with a small number of spatial modes.Multitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
The brain continually learns tasks with very different computational demands.EnerGNN: Learning Optimization-Compatible Energy Functions for Exact Constrained Combinatorial Inference
Learning-based approaches to combinatorial optimization often predict solutions directly or use predictions to restrict search, while expressive energy-based models typically rely on approximate…Stable and Calibrated Latent Force ODE Filters
Estimating unobserved forces in mechanistic systems is a central task of scientific machine learning.Why Does Structured Output Fail? An Empirical Study of Prompt-Enforced Pydantic Schemas Across Nine LLMs
Pipelines that require structured outputs from hosted language models often specify a Pydantic schema in the prompt, validate the response, and retry when validation fails.Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing
Autonomous vertical landing of reusable launch vehicles is a critical yet formidable challenge in aerospace engineering, primarily due to highly nonlinear dynamics, stringent precision require-…GS-MoE: Training-Free MoE Compression via Groupwise Subspace Sharing and Pruning
Mixture-of-Experts (MoE) models reduce computation through sparse activation, but storing their expert parameters remains a major memory bottleneck.MAGIC: Margin-gated Contrastive Debiasing with Evidence Restoration for VLM Test-time Robustness Scaling
This paper studies the problem of test-time robustness scaling for large vision-language models (LVLMs), which aims to mitigate language bias during inference.