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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 217
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs
Probes on activation vectors are widely used to detect misbehavior in language models, but their success relies on the assumption that models cannot control what their activations encode.Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning
Tracking changing comparators is a central problem in online learning.Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities
This paper investigates the use of Behavior Cloning (BC) for autonomous driving from a bird's-eye view (BEV) perspective in simulated urban environments.HGRP: Heterogeneous Graph Neural Network for Routability Prediction
Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization.Continual pretraining Prostate MRI ViT Encoder for task alignment
Building foundation models for MRI has emerged as a promising direction for advancing medical image analysis and improving accessibility of task specific models.Mixed Poisson diffusion models and application to posterior sampling
Diffusion models are a powerful framework for learning complex probability distributions.The minimal width of neural networks and immersions of real projective space
We investigate the minimal width of deep neural networks that can approximate all continuous functions from $\mathbb{R}^n$ to $\mathbb{R}^m$ in the topology of uniform convergence on compact subsets.MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning
Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially.Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications.Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation
Multilingual models share capacity across languages, so adding languages can hurt performance on existing ones.Contrastive uncertainty learning for robust multi-structure segmentation in lower-pelvic MR
Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity.Adversarial Reinforcement Learning for Robust Planetary Landing
Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness.Long-Horizon RL Needs Muon
Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions.Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics
Deploying a general-purpose model as an agent means running it inside a product-specific runtime harness that decides which actions are executable, how state advances, and when the loop may terminate.FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning
Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an attractive solution…FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation
Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency.Resolution Allocation with Learnable Resizing and Spectral Filter Parameterization
Learnable downsampling and spectral-domain filter parameterizations have each made convolutional networks more flexible, but their interaction has not been systematically examined.TANGO: RNA Topology and Geometry Co-Design
Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthetic biology and therapeutics.From Plato to Aristotle: Toward a General Theory of Representation Hypotheses
The Platonic Representation Hypothesis (PRH) holds that increasingly capable models converge toward a common representation.Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects
Conditional generative models are increasingly used for scientific prediction beyond the direct training support.