Optimal Stopping

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Overview

Optimal stopping asks when to take an irreversible action as observations arrive, trading the value of waiting for more information against time, sampling, or computation costs. Classical work develops threshold policies and dynamic programming for problems such as American options, secretary problems, and prophet inequalities; newer methods learn stopping boundaries with reinforcement learning, Bayesian optimization, or sequential tests. Applications include deciding when to stop medical workups, measurements, model evaluation, or language-model reasoning, where accuracy and resource use both matter. A central challenge is making learned policies reliable when costs, environments, and evidence change, while providing calibrated guarantees on regret, error, or safety.

Latest papers

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39 papers5 papers in the last four weeks, down 44% on the four weeks before. 0.0% of all new papers.

  1. The Staged Pandora's Box: Reservation Indices for Multi-Fidelity Bayesian Optimization

    Nov 2, 2026Long Vu, Jongseong Brad Choi, Binoy Ravindran +1Optimal StoppingBayesian Optimization

    State University of New York Korea · Virginia Tech · Stony Brook University

  2. Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?

    Oct 30, 2026Long Vu, Jongseong Brad Choi, Binoy Ravindran +1Bayesian OptimizationOptimal Stopping

    State University of New York Korea · Virginia Tech · Stony Brook University

  3. QD-PINN:A Physics-Informed Neural Network Approach to Bayesian Quickest Detection in High-Dimensional Diffusions

    Oct 27, 2026Hang Ye, Yi Liu, Haipeng XingOptimal StoppingNeural PDE Solvers

    Department of Applied Mathematics and Statistics, Stony Brook University

  4. Waiting Buys Exactly Two Moments: Endogenous Identification in Prediction-Driven Allocation

    Oct 16, 2026Rama AlHamidi, Rasul Khanbayov, Erchin Serpedin +1Sequential Decision MakingPredict-Then-Optimize

    Department of Computer and Electrical Engineering, Texas A&M University at Qatar, Doha, Qatar · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Department of Computer and Electrical Engineering, Texas A&M University, College Station, TX, USA

  5. Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning

    Oct 15, 2026Ke Xiao, Qiyuan Wang, Christos AnagnostopoulosFederated LearningEarly Stopping

    School of Computing Science, University of Glasgow

  6. Valid Stopping in Adaptive Generator-Verifier Loops

    Oct 5, 2026Mahmoud Hegazy, Michael I. Jordan, Aymeric DieuleveutFDR ControlE-Values

    CMAP, École polytechnique, IP Paris, France · Inria Paris, France · EECS, UC Berkeley, USA

  7. OR for AI That Does OR: Routing LLMs up the Escalator inside the OSCAR Framework

    Oct 1, 2026Jinzhi Bu, Haixin Tang, Huanan ZhangLarge Language Model-Guided OptimizationOptimal Stopping

    Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong · Leeds School of Business, University of Colorado Boulder, Boulder, CO, 80309, USA

  8. Budget Boundary Effects in Test-Time Mathematical Reasoning

    Sep 30, 2026Guilin Zhang, Ziqi Tan, Wulan Guo +5Mathematical Reasoning BenchmarksTest-Time Optimization

    Yantong AI

  9. Anticipatory Robot Goalkeeping via Monotone Optimal Stopping

    Sep 21, 2026Hao E. Zhang, Ruize Geng, Yisen Li +8Robotic CatchingOptimal Stopping

    Carnegie Mellon University · University of Texas at Arlington · General Motors

  10. PROSE: A Theory of Optimal Stopping with Perishable Evidence for Peer Selection in Intermittently Connected Decentralised Learning

    Sep 20, 2026Christos AnagnostopoulosOptimal StoppingDecentralized Learning

    School of Computing Science, University of Glasgow, Glasgow, United Kingdom.

  11. SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning

    Sep 20, 2026Ke Xiao, Qiyuan Wang, Christos AnagnostopoulosOptimal StoppingAdaptive Sampling

    School of Computing Science, University of Glasgow, Glasgow, UK

  12. Optimal No-Regret Learning for Repeated Prophet Inequality

    Sep 20, 2026Kun WangRegret MinimizationOptimal Stopping

    Purdue University

  13. Multi-Armed Bernoulli Bandits via Minimax Single-Arm Stopping

    Sep 19, 2026Huikang Liu, Zhengchao Wang, Daniel Kuhn +1Multi-Armed BanditsMinimax Regret

    Shanghai Jiao Tong University · The University of Sydney · École Polytechnique Fédérale de Lausanne +1

  14. How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing

    Sep 18, 2026Vincent Corlay, Andriy EnttselActive PerceptionOptimal Stopping

    Mitsubishi Electric R&D Centre Europe

  15. Safe to Stop? Risk-Constrained Stopping for Sequential Clinical Diagnosis Agents

    Sep 9, 2026Yuexin Wu, Vasile RusSelective PredictionAI Agent Evaluation

    Department of Computer Science University of Memphis

  16. Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

    Aug 25, 2026Nadeem ShaikhAdaptive Model RoutingLLM Agent Orchestration

    Independent Researcher Melbourne, Australia

  17. Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations

    Aug 14, 2026Toby D. PilditchLLM EvaluationOptimal Stopping

    UK AI Security Institute, London, UK

  18. Optimal Stopping of Self-Refining Foundation Models

    Aug 11, 2026Kim Hammar, Tansu Alpcan, Emil C. LupuLLM Self-RefinementOptimal Stopping

    Imperial College London, United Kingdom · University of Melbourne, Australia

  19. Kernel Methods for Refined Prophet Inequalities

    Aug 9, 2026Patrick Loiseau, Mathieu Molina, Vianney Perchet +2Optimal Stopping

    FairPlay Joint Team, Inria · Tel-Aviv University · FairPlay Joint Team, CREST, ENSAE, IP Paris, CRITEO AI Team +2

  20. LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations

    Jul 31, 2026Yan Fang, Jialin Chen, Chun Gan +5Mechanism DesignOnline Advertising

    JD.com, Inc. · Shanghai University of Finance and Economics · University of Sydney +1

  21. CC-AOS: Cost- and Horizon-Conditioned Amortized Backward Induction for Finite-Horizon Optimal Stopping

    Jul 24, 2026Tianwei YuTime Series ClassificationCost-Aware Inference

  22. OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping

    Jul 13, 2026Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias +2Efficient Language Model ReasoningSequential Decision Making

    MIT · Columbia Business School

  23. How much Data do We Need? Sequential Data Collection for Stochastic Programming

    Jul 11, 2026Xin Li, Juergen Branke, Xuan Vinh DoanStochastic OptimizationSequential Decision Making

    University of Warwick

  24. Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data

    Jul 9, 2026Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3Early StoppingOptimal Stopping

    IBM Research

  25. Strategic Buying Agents

    Jul 6, 2026Mingyang Fu, Ming HuSequential Decision MakingOptimal Stopping

    Rotman School of Management, University of Toronto

  26. When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models

    Jun 29, 2026Zhe Dong, Fang Qin, Manish ShahEfficient Language Model ReasoningCost-Aware Inference

    University of Maine at Presque Isle · Stanford University · Independent Researcher

  27. Asymptotically Optimal Learning for Parametric Prophet Inequalities

    Jun 25, 2026Jung-hun Kim, Anna Grebennikova, Vianney PerchetSequential Decision MakingOptimal Stopping

    FairPlay Team, CREST, ENSAE, Institut Polytechnique de Paris · UFR IM2AG, Université Grenoble Alpes · Criteo AI Lab

  28. Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding

    Jun 20, 2026Xuanming Zhang, Sining Zhoubian, Yuxuan Chen +8LLM AlignmentLanguage Model Decoding

    Qwen Team, Alibaba Inc. · Tsinghua University · Nanyang Technological University

  29. Continuous-time Optimal Stopping through Deep Reinforcement Learning

    Jun 16, 2026Cosmin Borsa, Michael LudkovskiQuantitative FinanceSequential Decision Making

    Department of Statistics & Applied Probability, UC Santa Barbara. Santa Barbara, CA 93106-3110, USA

  30. FAST-ME: Foundation-aware Adaptive Stopping for Motion Estimation for Efficient IoT Video Analysis

    May 22, 2026Kakia Panagidi, Stathes HadjieftymiadisSequential Decision MakingOptimal Stopping

    Department of Informatics & Telecommunications National and Kapodistrian University of Athens Athens, Greece