Multi-Fidelity

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5 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 25

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Canopy: Exploiting Piecewise Smooth Tree Priors for Multi-Fidelity Bandits
Sep 24, 2026Michael Jerge, Suman JanaTree Search Multi-Fidelity
Amazon · Columbia University
MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
Sep 24, 2026Lucas Palazzolo, Mickaël Binois, Laëtitia GiraldiMulti-Fidelity Bayesian Optimization
Université Côte d’Azur, Inria, Calisto team, Sophia Antipolis, France · Université Côte d’Azur, Inria, Acumes team, CNRS, LJAD, Sophia Antipolis, France
MICRO: Multi-Fidelity Active Search for Severe Error Discovery
Sep 22, 2026Orlando Leone, Niclas Pokel, Pehuén Moure +2Feedback Discovery
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland · ETH AI Center, Switzerland · Department of Computational Linguistics, University of Zurich, Switzerland
Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
Sep 15, 2026Niki Triantafyllou, Andrea Bernardi, Maria M. PapathanasiouMulti-Fidelity Multi-Objective Bayesian Optimization Algorithms
Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
Revisiting Multi-Object Tracking Baselines: Hyperparameter Optimization with Multi-Fidelity Greedy Coordinate Search
Sep 14, 2026Momir AdžemovićMulti-Object Tracking Multi-Fidelity
Department of Computer Science, Faculty of Mathematics, University of Belgrade, Studentski trg 16, Belgrade, 11000, Serbia
Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
Aug 4, 2026Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval +2Multi-Fidelity Bayesian Optimization
Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada · Vector Institute, Toronto, ON, Canada · Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada +2
Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design
Jul 8, 2026Sergei Zorkaltsev, Maciej Haranczyk, Christina SchenkBayesian Optimization Multi-Fidelity
1IMDEA Materials Institute, Eric Kandel 2, Getafe, 28906, Madrid, Spain. · Department of Materials Science and Engineering and Chemical Engineering, Universidad Carlos III de Madrid, Av. Universidad 90, Leganes, 28911, Madrid, Spain.
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
Jun 29, 2026Zhuhan Bao, Rui Yang, Bohao Yang +16Simulated Patients Conversational Datasets
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA · Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore · Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore, Singapore +16
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Jun 25, 2026Santosh Kapuria, AbhishekWavelets Physics-Guided Machine Learning
Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India
SOLAR: AI-Powered Speed-of-Light Performance Analysis
Jun 24, 2026Qijing Huang, Sana Damani, Zhifan Ye +9Smooth Execution Deep Learning
NVIDIA
Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems
Jun 23, 2026Niklas Dahlquist, Shridhar Velhal, George NikolakopoulosMulti-Robot Systems First-Price Auctions
Luleå University of Technology, Luleå, SE-971 87 Sweden
Field-level weak lensing cosmology with $60$ simulations using multifidelity simulation-based inference
Jun 22, 2026Alex A. Saoulis, Kiyam Lin, Niall Jeffrey +5Cosmology Multi-Fidelity
Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom · Department of Earth Sciences, University College London, 5 Gower Place, London, WC1E 6BS, United Kingdom · Department of Physics & King’s Institute for Artificial Intelligence, King’s College London, Strand, London WC2R 2LS, United Kingdom +3
Simulation-Based Multi-Fillet Evaluation of Woody Breast Poultry Fillets
Jun 15, 2026Chirantan Sen Mukherjee, Seung-Chul Yoon, William J. BeksiMulti-Fidelity Bird
The authors are with the Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA. · The author is with the Quality and Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA Agricultural Research Service, Athens, GA, USA.
Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Jun 1, 2026Vivin Vinod, Peter ZaspelQuantum Chemistry Multi-Fidelity
School of Mathematics and Natural Sciences, University of Wuppertal, Germany.
Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows
May 27, 2026Jice Zeng, Shady E. Ahmed, David Barajas-Solano +1Reduced-Order Models Reynolds-Averaged Navier-Stokes
Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, 99352, WA, USA
Multi-Fidelity Learning with Shallow Recurrent Decoders for Reactor Physics
May 22, 2026Stefano Riva, Carolina Introini, J. Nathan Kutz +1Multi-Fidelity Nuclear Data
Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University, Abu Dhabi (127788), United Arab Emirates
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantPartial Differential Equations Multi-Fidelity
Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
May 6, 2026Saghar Zolfaghari, Yu Xie, Junfeng Yang +1Combustion Control Multi-Fidelity
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA · School of Mechanical Engineering, University of Leeds, Leeds, UK
Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov-Arnold Networks
May 4, 2026Md Abdullah Al Mazid, Naphtali RisheAtmospheric Dynamics Solar Irradiance
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
May 4, 2026Haizhou Wen, Elham Kiyani, Gang Li +3Multi-Fidelity Anisotropic
Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA · Division of Applied Mathematics, Brown University, Providence, RI 02912, USA · Center for Composite Materials, University of Delaware, Newark, DL 19716, USA +4
Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling
Apr 24, 2026Matilde Fiore, Andrea Bresciani, Miguel Alfonso Mendez +1Offshore Wind Turbine Multi-Fidelity
Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, Sint-Genesius-Rode, B-1640, Belgium · Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, Av. Franklin Roosevelt 50, Brussels, 1050, Belgium · Aerospace Engineering Research Group, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, 28911, Spain
Adaptive multi-fidelity optimization with fast learning rates
Apr 17, 2026Come Fiegel, Victor Gabillon, Michal ValkoMulti-Fidelity Batch
´Ecole Normale Sup´erieure, Paris · Huawei R&D, UK · Inria Lille
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Mar 23, 2026Atticus Rex, Elizabeth Qian, David PetersonMulti-Fidelity Surrogate Models
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA · Air Force Research Lab, Wright-Patterson AFB, Dayton, Ohio
Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
Mar 17, 2026Isaac Robledo, Alberto Vilariño, Arnau Miró +3Multi-Fidelity Aerodynamics
Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · TUAREG Research Group Universitat Politècnica de Catalunya (UPC) CASE Large-Scale Computational Fluid Dynamics Barcelona Supercomputing Centre (BSC) Barcelona, Spain
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Apr 2, 2025Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1Multi-Fidelity Bayesian Inference
Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA · Fusion Energy Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA

Sep 24, 2026, cs.LGJ/K move · Enter open · S save

Canopy: Exploiting Piecewise Smooth Tree Priors for Multi-Fidelity Bandits

Michael Jerge, Suman Jana

Amazon · Columbia University

Tree Search Multi-Fidelity Prior Knowledge Integration Trees LLM Inference Optimization Canopy

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Many LLM inference problems, including model routing, prefix-cache management, prompt trimming, and test-time search, can be viewed as optimization over a tree. This structure arises naturally from autoregressive generation: every prefix defines a node, and its continuations form a subtree below it. Internal nodes of the tree provide cheap but biased estimates of a region's value, while leaf evaluations are expensive but accurate. Hierarchical bandit methods can exploit this structure, but typically require a specific smoothness schedule to be specified in advance, even though real objectives are often only piecewise smooth and their optima may lie near sharp boundaries. We introduce CANOPY, a multi-fidelity tree bandit that learns where the smoothness prior is valid rather than assuming it globally. CANOPY uses cheap random-path probes to construct an online certificate of local aggregation bias, then directs expensive leaf evaluations toward cells where the certificate detects a smoothness violation. We prove fixed-budget and regret guarantees whose additional cost is additive in the number of discontinuities, recovering the smooth-tree rate when no violations are present and approaching structure-blind search as violations become dense. Across routing, top-

k

identification, test-time search, caching, and prompt trimming, CANOPY consistently improves matched-budget performance, including

2.9\times

higher top-10 recall on a 1000-model pool,

1.6\times

more SWE-bench Verified issues resolved than best-of-

N

, and

3.6\times

lower median time-to-first-token with prefix caching.

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