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.
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Newest papers, page 18
Credibility Clustering for Experience Rating: An Automated Approach to Risk Group Allocation in Group Life Insurance
In insurance, experience rating plays a crucial role in mitigating adverse selection by adjusting premiums according to historical claims experience.Regulating Autonomous Mobility-on-Demand Operators with Fixed and Adaptive Penalties: A Multi-Agent Reinforcement Learning Approach
Profit-maximizing Autonomous Mobility-on-Demand (AMoD) operators may exploit consumer surplus through excessive prices and underserve less profitable regions by relocating vehicles away from them.PromptOps: Iterative Prompt Optimization for LLM-as-a-Judge Systems
Large language models (LLMs) are increasingly used as automated judges for evaluating the behavior of chatbots and other AI systems.Machine Learning for Live-Birth Prediction After IVF/ICSI From Structured Clinical Data: A Systematic Review, Meta-Analysis, and Critical Appraisal
Machine-learning models predicting live birth after in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) from routine clinical data report performance from near chance to near…Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data
Molten chloride corrosion has been studied for decades, yet reliable prediction across different alloys, salts, and test conditions remains difficult.Scalable Bayesian Gaussian Process Classification for Cancer Histopathology: Calibrated Uncertainty Under Distribution Shift
Reliable uncertainty quantification is essential for clinical deployment of cancer diagnosis models, yet neural networks and variational Gaussian process methods can produce overconfident…Bayesian Tensor Train Kernel Machines with Automatic Model Complexity Inference
Tensor Network (TN) kernel machines accelerate supervised learning by using low-rank TNs to reduce computation and memory.Teaching Agents the "Principle", Not the "Reward"
Reinforcement learning has achieved strong performance in many tasks by letting agents learn from nothing more than a reward signal.A Proxy-Based Method for Measuring Racial Disparities in~Text-to-Image~Generators
We present an automated method for auditing text-to-image generators for representational bias against ground-truth demographic benchmarks.Context-Aware Automated NMR Structure Elucidation
Machine learning approaches are rapidly revolutionizing the ability to analyze and extract structural insights from chemical data, enabling high-throughput chemical discovery.FastAFD: When Does Attention-FFN Disaggregation Pay on Rack-Scale NVLink?
Attention-FFN disaggregation (AFD) separates attention from the experts of a mixture-of-experts (MoE) model and has been reported to raise decode throughput over colocated serving.Depth or Description? When a One-Step Latent Score Replaces Lookahead in Proactive Information Acquisition
A proactive agent must decide what to ask before it acts.A Survey of Nonlinear Filtering Methods for Data Assimilation and Recent Advances in Generative AI-Enabled Score Filters
Many real-world applications, such as numerical weather prediction or wildfire tracking, involve modeling highly nonlinear dynamical systems that are chaotic and influenced by various sources of…Structural Conditional Independence Test via Neural Quantile Resolution
Conditional independence testing is difficult when dependence is weak, localized, or expressed through distributional changes rather than a global mean shift.Multi-step SWE: Can Coding Agents Keep Building on Their Own Code Over Long Horizons?
Developers rarely give a coding agent a single request: they ask it to build a component and then to extend, upgrade, and optimize it, each time on code the agent itself wrote.Meta-Frieren: Beyond Single Level Online Learning
We study a two-stage online optimization problem: at each round, a meta-learner first selects an agent-environment pair, then the chosen agent interacts with the environment, and receives a reward.ShapleyOG: Fast Adaptive Shapley Value Estimation with Odd Gaussian Processes
Shapley values are a central tool for attribution in machine learning.Backbone-Informed Adaptation after EEG Pretraining: A Capacity-Controlled Study
Electroencephalography (EEG) foundation models learn representations that combine information across channels and time.TrustMix: Graceful Degradation for Pretrained Models under Distribution Shift
Predicting beyond the training domain necessarily requires assumptions about how the ground truth extrapolates, yet in most neural networks these assumptions are made only implicit through the…Structured Machine Theory of Mind from Agent Trajectories
Predictive models of human behavior trained on large-scale trajectory data optimize for statistical accuracy without representing the mental states that causally generate behavior.