Prior-Data Fitted Networks

Also known as PFN

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Continual Learning without Continual Training
Oct 7, 2026Nikita Narayanan, Ritham Majumdarr, Sonali ParbhooContinual Learning Class-Incremental Learning
Imperial College London
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Oct 6, 2026Christophe Muller, Ayub Kharel, Alex Luedtke +6Amortized Inference Conditional Average Treatment Effect Estimation
University of Oxford · Harvard University · University of Illinois Urbana-Champaign +5
Adaptive Mean Estimation by In-Context Learning: A Gradient-Flow Analysis
Oct 6, 2026Martin Eppert, Krishna Balasubramanian, Subhro Ghosh +2In-Context Learning Prior-Data Fitted Networks
National University of Singapore · University of California, Davis · Princeton University
Adapting prior-data fitted networks for tabular anomaly detection
Oct 5, 2026Maximilian Bershtman, Niv CohenTabular Anomaly Detection Unsupervised Anomaly Detection
Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa, Israel
LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Oct 5, 2026Louis Schiekiera, Max Zimmer, Christophe Roux +3Model Selection Prior-Data Fitted Networks
Institute of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany · Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany · Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany +1
IQS-BO: In-Context Query Selection for Bayesian Optimisation
Oct 1, 2026Luca Geminiani, Nadja KleinBayesian Optimization Prior-Data Fitted Networks
Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT)
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Jul 31, 2026Mohammad Sadeq Abolhasani, Viswanath GanapathySynthetic Data Pretraining Synthetic Data Generation
SAP Labs, LLC. Palo Alto, CA, United States
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
Jul 26, 2026Yuche Gao, José Miguel Hernández-Lobato, Siyuan GuoSynthetic Data Pretraining Structural Causal Models
University of Cambridge, Cambridge, United Kingdom · Prior Labs, Freiburg, Germany
TabPFN beyond Tabular Data: Calibration and Accuracy on Multimodal Embeddings
Jul 13, 2026Jingxiang Zhang, Lujia Zhong, Zijie Zhu +2Multimodal Embedding Multimodal Classification
University of Southern California
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Jun 23, 2026Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev +1Graph Neural Networks Graph Foundation Models
1HSE University · 2Yandex Research
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Jun 19, 2026Franco Martino O'Rourke, Ana Trisovic, Dimitris BertsimasStructural Causal Models Multivariate Time Series Classification
Massachusetts Institute of Technology, Cambridge, MA, USA.
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Jun 19, 2026Zhengkang Guan, Yikang Chen, Yi He +5Causal Foundation Models Structural Causal Models
College of Computer Science and Technology Zhejiang University
Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals
Jun 18, 2026Shravan Talupula, Saurabh SharmaCausal Foundation Models Causal Effect Estimation
ProfitOps Inc.
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
Jun 9, 2026Jamie Heredge, Mattia J. Villani, Pranav Deshpande +2In-Context Learning Prior-Data Fitted Networks
Global Technology Applied Research, JPMorganChase, New York, NY 10001, USA
$α$ -PFN: Fast Entropy Search via In-Context Learning
Jun 5, 2026Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering +4Bayesian Optimization Prior-Data Fitted Networks
University of Helsinki · University of Freiburg · Delft University of Technology +2
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Jun 4, 2026Amirhossein Zare, Amirhessam Zare, Herlock Rahimi +2Structural Causal Models Counterfactual Prediction
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran. · Department of Electrical and Computer Engineering, Yale University, New Haven, CT, USA. · School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. +2
SurvPFN: Towards Foundation Models for Survival Predictions
Jun 3, 2026Samuel Böhm, Lennart Purucker, Frank Hutter +1Tabular Foundation Models Survival Analysis
Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany · Institute of Epidemiology and Prevention, Medical Center – University of Freiburg, Freiburg, Germany · Department of Computer Science, University of Freiburg, Freiburg, Germany +7
On the Uncertainty Quantification Ability of Tabular Foundation Models
May 31, 2026Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2Tabular Foundation Models Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, University of California, Irvine · Department of Civil and Environmental Engineering, University of California, Irvine
Towards Continuous-time Causal Foundation Models
May 26, 2026Dennis Thumm, Ruben Wiedemann, Ying ChenIrregular Time-Series Modeling Causal Foundation Models
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore. · Imperial College London
Transformers Can Learn Posterior Predictive Distributions In-Context
May 26, 2026Gyeonghun Kang, Changwoo J. Lee, Xiang ChengBayesian Inference Transformer Attention
Department of Statistical Science, Duke University, Durham, NC, USA · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification
May 20, 2026Samuel McDowell, Nathan Stromberg, Lalitha SankarClass-Imbalanced Learning Tabular Classification
School of Electrical, Computer and Energy Engineering · Arizona State University, Tempe, AZ
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
May 19, 2026Xinpeng Lv, Yunxin Mao, Renzhe Xu +13Tabular Foundation Models Strategic Classification
College of Computer, National University of Defense Technology, Changsha, China · Institute for Theoretical Computer Science, Shanghai University of Finance and Economics, Shanghai, China · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China +3
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
May 15, 2026Shi-ang Qi, Vahid Balazadeh, Michael Cooper +2Amortized Inference Survival Analysis
Vector Institute · University of Toronto · University of Alberta +1
PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
May 11, 2026Yan Shuo Tan, Kenyon Ng, Ruizhe Deng +3Thompson Sampling Prior-Data Fitted Networks
National University of Singapore · Monash University · Renmin University of China +1
Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation
May 8, 2026Elliot Pickens, Chiraag Gohel, Sidharth SatyaTabular Foundation Models Simulation-Based Inference
University of Pennsylvania · The George Washington University · Brown University
Decoupled PFNs: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors
May 7, 2026Richard Bergna, Stefan Depeweg, José Miguel Hernández-LobatoUncertainty Quantification Bayesian Optimization
University of Cambridge · 2Siemens AG
Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
Apr 29, 2026Brad Ye, Bulent Soykan, Gulsah Hancerliogullari Koksalmis +2Tabular Foundation Models Alzheimer's Disease
Department of Medicine, University of Central Florida College of Medicine, Orlando, FL, USA · NIMBUS Lab, University of Central Florida · Department of Mechanical, Industrial & Manufacturing Eng., The University of Toledo, Toledo, OH, USA +3
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
Apr 19, 2026César Ojeda, Niklas Hartung, Wilhelm Huisinga +6Zero-Shot Time Series Forecasting Healthcare
University of Potsdam, Germany · Technical University Berlin, Germany · Freie Universität Berlin, Germany +2
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Sep 25, 2025Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2Synthetic Data Pretraining In-Context Learning
1HSE University · 2Yandex Research

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Continual Learning without Continual Training

Nikita Narayanan, Ritham Majumdarr, Sonali Parbhoo

Imperial College London

Continual Learning Class-Incremental Learning Domain-Incremental Learning Prior-Data Fitted Networks In-Context Learning

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Continual learning requires models to adapt to new domains and new classes while retaining prior knowledge. Many existing methods rely on continued optimization, using regularization, replay, or parameter expansion to prevent new updates from overwriting previously learned knowledge. Instead, we propose replacing continual training with continual inference: a PFN-based model that is meta-trained, and then frozen, adapting to new classes only by extending an in-context evidence set. Our model, Latent Concept PFN, performs in-context Bayesian inference over a latent concept space that captures semantic structure shared across domains and classes. As each new domain or class arrives, exemplars are added to the memory; adaptation reflects updated posterior beliefs over latent concepts rather than gradient updates. No parameters are changed, reducing forgetting. The same method handles both domain and class incremental continual learning without task identity. Concept annotations are only used during meta-training, acting as a soft anchor on the latent space rather than a fixed bottleneck. Unlike fixed-vocabulary concept methods, the model also handles noisy, ambiguous, or incomplete annotations by combining concept labels with raw input evidence to discover distinctions beyond the predefined concept set. Experiments on class and domain incremental learning datasets demonstrate competitive continual learning performance while learning interpretable latent concepts.

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