Optimal Stopping

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Latest papers 34

9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents
Mar 31, 2026Davide Di GioiaTool-Using Agents Cognitive Architectures for AI Agents
A Causal Framework for Evaluating ICU Discharge Strategies
Mar 26, 2026Sagar Nagaraj Simha, Juliette Ortholand, Dave Dongelmans +4Healthcare Sequential Decision Making
Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, NL · Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, NL · Department of Intensive Care, Elisabeth-TweeSteden Hospital, Tilburg, NL +3
Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection
Mar 16, 2026Kensuke Muto, Hirotaka Sakamoto, Kenji Nagata +2Optimal Stopping
Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277–0882, Japan · Karakuri, Inc., 2-7-3 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan · Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305–0047, Japan +1
Learning Optimal Search Strategies
Mar 2, 2026Stefan Ankirchner, Maximilian Philipp ThielOptimal Stopping Minimax Regret
Institute for Mathematics, University of Jena, Inselplatz 5, 07743 Jena, Germany.

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Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents

Davide Di Gioia

Tool-Using Agents Cognitive Architectures for AI Agents Optimal Stopping Value of Information Bayesian Decision Theory

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Autonomous tool-using agents in networked environments must decide which information source to query and when to stop querying and act. Without principled bounds on information-acquisition costs, unconstrained agents exhibit systematic failure modes: excessive tool use under congestion, prolonged deliberation under time decay, and brittle behavior under ambiguous evidence. We propose the Triadic Cognitive Architecture (TCA), a decision-theoretic framework that formalizes these failure modes via cognitive friction. By combining nonlinear filtering, congestion-dependent cost dynamics, and HJB optimal stopping, TCA models deliberation as stochastic control over a joint belief-congestion state, explicitly pricing information by tool signal quality and live network load. TCA yields an HJB-inspired stopping boundary and a computable rollout-based approximation of belief-dependent value-of-information with a net-utility halting condition. We validate TCA in two controlled environments (EMDG and NSTG) designed to isolate stopping quality, action selection under congestion, and temporal urgency. TCA improves resource outcomes while reducing time-to-action without degrading accuracy, gaining 36 viability points in EMDG and 33 integrity points in NSTG over greedy baselines. Ablations show that selection and stopping must be optimized jointly, as stopping rules alone recover at most 4 viability points. Sensitivity sweeps over alpha, beta, and lambda_S yield stable accuracy and interpretable trade-offs, and a continuation-value sweep over eta values 0, 0.1, 0.3, and 0.5 finds eta equal to zero is optimal under high temporal urgency. Finally, we demonstrate an illustrative instantiation around a black-box LLM on a memorisation-free corpus, where the same stopping principle executes using empirically computable uncertainty and value-of-information proxies.

Latest papers 34

Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents

A Causal Framework for Evaluating ICU Discharge Strategies

Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection

Learning Optimal Search Strategies