Probability & Statistics · Mathematics & Statistics
Applications
The newest papers in Applications that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
143 papers, from 4 September 2026 to 5 November 2026: 143 arXiv preprints.
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Is Your LLM Relay Serving What It Claims? A low-cost and high-power test for LLM Relay Auditing
Relay and reseller APIs have become a common way to access large language models (LLMs), yet users cannot directly verify whether they receive the models they request and pay for.An Application of Geometric Riemannian Pricing Algorithm for Valuing Catastrophe Bonds in Incomplete Markets: Market-Consistent Financial Entropy and the Split of Frequency and Severity Risk Premia
This paper presents a framework for pricing catastrophe (CAT) bonds in incomplete markets through Riemannian geometric optimization of financial entropy.Combining Multiple Incomplete Astronomical Surveys to Estimate Galaxy Flux Distributions
In astrophysics, the distributions of fluxes or intensities of populations of sources provide insight into the structure and evolution of the universe.Intervene, Calibrate, Discover: Learning Causal Structure from Pixels
Learning high-level causal variables from low-level observations is a central problem for artificial intelligence.Accuracy and efficiency of compact foundation models for surface-water mapping during floods
Pretrained Earth observation models may reduce the effort needed to map surface water, but their value depends on the alternative against which they are compared.Causal DAG-Guided Reinforcement Learning for Long-Term Physical Activity Maintenance: Using Prior Data to Inform Future Deployment
Digital health interventions can provide scalable, personalized support for physical activity (PA), yet sustaining PA over the long-term remains difficult.Resolving the Mobility-Equity-Emissions Trilemma: A Causal Machine Learning Framework for Dynamic Tolling Policy
We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma.Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks
Compiling implied volatility (IV) surfaces and forecasting derivative risk from high-frequency equity and options feeds requires addressing contract heterogeneity, schema shifts, volatility…Allocation Before the Clock: Graph, Set and Game Theory of Professional League of Legends Objective Setups
Before every neutral objective in professional League of Legends, each team decides which of its five players walk to the pit, which hold a lane, and when to commit.The Computational Assessment Stack: An Architecture for AI-Native Measurement
Test development is shifting toward automation and agentic artificial intelligence (AI), in which autonomous systems draft items at scale, assemble test forms, administer adaptive assessments, score…Not All Mislabels Are Equal: Efficient Task-Aware Auditing with Confidence Sequences
We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream…Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the…Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome.Artificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
Artificial intelligence (AI) is reshaping mechanical engineering (ME), from modeling, simulation, and optimization to diagnostics, design, and technical communication.StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves
Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help.A Bayesian machine learning approach to direct and spillover effects in cluster-randomized trials with noncompliance
We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance.Beyond Scalar Leaderboards: Adaptive Sampling for Pareto Frontier Identification in Multi-Objective LLM Arenas
The evaluation of large language models (LLMs) is inherently multi-objective, as different models may offer different tradeoffs across performance dimensions such as math, code, and writing.BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous…Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health.Keep the Lineages Alive: Diversity-Preserving SMC for Diffusion LLM Decoding
Inference-time sequential Monte Carlo (SMC) decoding can improve the reasoning accuracy of masked diffusion language models (diffusion LLMs) without post-training by resampling a population of…