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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 76
Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
Building a language model of one's own no longer requires a data centre.TRACER, a time-to-event EHR foundation model for disease detection across health systems
Foundation models trained on longitudinal electronic health records (EHRs) can predict clinical diagnoses years in the future, making them a potential tool for disease screening.Machine Learning-Based Detection of Mobile Money Scan Messages in Ghana: A Comparative Evaluation of Naive Bayes, Support Vector Machine, and Randon Forest Classifiers
Mobile money (MoMo) is Ghana's preferred retail payment channel, recording GHC518.4 billion in transactions in December 2025 and over 26.7 million active accounts (Graphic Online, 2026).Training Foundation Models with a Single Algorithm
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms.Gym-Anything for Robotics: Scaling physical simulations with digital agents
Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real world.Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond
Integrated sensing and communications (ISAC) systems using 5G and beyond signals of opportunity enable joint wireless sensing and communication using shared infrastructure and spectrum resources.Difficulty Calibration Across Time for Membership Inference in Diffusion Models
Modern diffusion models are trained on massive datasets that often include copyrighted or private images.An ODE-based Sampler for Discrete Diffusion Models
The marginals of a continuous diffusion model can be reproduced by a deterministic ordinary differential equation (ODE), known as the probability flow ODE, which provides a foundation for…world2vec: Mechanistic world models through latent dynamics learning
World models can achieve strong control performance without faithfully capturing the dynamics of their environment, limiting their potential for mechanistic interpretability and scientific discovery.Adaptive Scenario Generation for KL-DRO Around a Learned Density Model
Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in…Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers
Pixel-space generative Transformers typically use one token per image patch, so capturing finer detail requires more tokens and makes global attention increasingly expensive.Rethinking Neural Thickets Without Weight Perturbations
The Neural Thickets hypothesis posits that pretrained language models lie in dense neighborhoods of task-specific experts in weight space, which random Gaussian perturbations can uncover.Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering
Differential privacy requires that algorithms be stable to the change of any record in their input, even to an extreme outlier.The Geometry of Multitask Learning: Sharp Rates for Shared Subspace Estimation
Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain poorly understood in many of the formal settings considered in the literature.PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management
Autonomous LLM agents executing multi-step workflows over personal data often misalign with user privacy expectations and leak sensitive, out-of-context information.INLA for Adaptive UAV Sampling of Radio Environment Maps in IoT Networks: Prior Width Governs Accuracy and Calibration
We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations.Li$^{2}$SAO: Lifting Light Single-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping
Asynchronous reinforcement learning (RL) is becoming a mainstream paradigm for post-training large language models (LLMs), as it improves training efficiency by updating the model as rollouts arrive.Bayesian Distribution System State Estimation with Flow Matching
Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms.Don't Just Impute, Detect: Task-Aware Diffusion Pipelines for Edge Anomaly Detection
Industrial Internet of Things (IIoT) edge pipelines routinely lose contiguous blocks of multi-sensor data to network dropouts and hardware faults.Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data
In the search for new particles in high-energy physics, it is necessary to first define the signal region (SR) enriched in signal events, and then compare events in this region with the expected…