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 122
PASTA: Prompt-Aware Annealed Diffusion Sampling
While Diffusion models produce high-quality images, real-world visual design requires intentionally preserving clean regions for headlines, logos, or UI elements.When Does Dynamics Randomization Test Context Inference?
Environments with randomized dynamics are widely used to ask whether world models need explicit context inference.On the Limits of Stochastic Push-Pull over Time-Varying Digraphs
Stochastic Push-Pull achieves linear speedup with respect to the network size $n$ on static strongly connected digraphs, but whether this speedup persists on time-varying networks remains unclear.Data Quality Audit and Bivariate Temperature-Humidity Analysis of a Long-Term LoRaWAN Network: Anomaly Detection, PCA, Clustering and Machine Learning
Long-term environmental IoT/LPWAN deployments often aggregate heterogeneous data streams from diverse devices without systematic auditing of each variable's actual coverage prior to analysis-an…PROVE: Probabilistic Visual Grounding via Patch-Level Evidence for Vision-Language Models
Visual grounding with vision-language models such as CLIP typically produces a single deterministic relevance map for an image-query pair, without indicating whether the localisation is reliable or…Decoupling Patient Personas and Clinical Workflows: A Dual-Agent Approach to Realistic Medical Dialogue Generation
Pre-consultation medical dialogues are essential for collecting comprehensive patient information, but are costly to obtain at scale due to privacy and ethical constraints.DIGOM: Disentangled Interaction Graphs for Outcome prediction in Multimodal healthcare
Multimodal healthcare data contain relational information that extends beyond individual patient observations.DualGSL: Prior-Guided Graph Structure Learning with Node-Adaptive Prediction Fusion
Graph structure learning (GSL) adapts propagation graphs to prediction tasks, but the relative predictive value of feature-derived priors and learned relations can vary across nodes.Sharpness-aware Orthogonalized Momentum Algorithms
Adversarial sharpness penalty per iteration of a base optimizer, known as sharpness-aware minimization (SAM), is an accepted technique to improve generalization in deep learning.Online Continual Learning in Edge-Cloud Applications
Deployed machine learning models in real-world IoT and edge/cloud systems face a critical reliability bottleneck: the requirement for online adaptation to non-stationary data streams while operating…PathJev: Decision-Native Vision-Language Modelling for Histopathology
Vision-language models used for decisions in histopathology rarely return calibrated probabilities over the candidates a user cares about.Efficient Zero-Shot Video Anomaly Detection via Signal-Aware Adaptive Fusion (SAAF)
Deploying Vision-Language Models for video anomaly detection confronts a fundamental constraint.State Estimation as In-context Tabular Prediction
This letter poses state estimation (SE) as an in-context tabular prediction (ICTP) problem.A curated multi-endpoint drug-protein bioactivity dataset with construct-aware protein targets
Public drug-protein bioactivity resources commonly identify targets by UniProt accession, although one accession may represent distinct experimental sequences.Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping
Large language models serve applications with diverse inference budgets, but training and maintaining separate models for each budget incurs substantial costs.Random Jump Forests
Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models.DRIVE: Fine-tuning via Data Contribution- and Diversity-aware Weighting with Prior Regularization
Single-stage fine-tuning is pervasive, yet catastrophic forgetting persists when the source pre-training data is inaccessible.BAARC: Uncertainty-Driven Adaptive Partial Spectral Measurement to Full Reconstruction for Material Classification
Spectroscopic instruments with limited wavenumber coverage require accurate scan positioning or multiple measurements to complete the material analysis.Quantum-Classical Link Prediction on Dark Web Hyperlink Graphs
We analyze HTML content and hyperlinks from an offline Tor crawl containing 383 onion hosts and 2,067 directed cross-host links.Amortized Thompson Sampling and Efficient Information-Directed Sampling for Simulator-Based Design
Thompson sampling is a widely used strategy for sequential decision-making under uncertainty, offering a principled way to balance exploration and exploitation.