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Awesome-Prior-Data-Fitted-Networks

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A curated collection of papers, repositories, and resources on Prior-data Fitted Networks (PFNs).

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# Awesome Prior-Data Fitted Networks

A curated collection of papers, repositories, and resources on **Prior-data Fitted Networks (PFNs)**.

## Table of Contents

- [What are PFNs?](#what-are-pfns)
  - [Example — TabPFN](#example--tabpfn)
- [Highlights](#highlights)
- [Foundations](#foundations)
- [Papers](#papers)
  - [On Tabular Data](#on-tabular-data)
  - [On Time Series](#on-time-series)
  - [On Graphs](#on-graphs)
  - [On Relational Databases](#on-relational-databases)
  - [On Causal](#on-causal)
  - [On AI4Science](#on-ai4science)
  - [All Papers](#all-papers)
- [GitHub Repositories](#github-repositories)
- [Other Applications](#other-applications)
- [Contributing](#contributing)
- [License & Credits](#license--credits)

## What are PFNs?

As the name says, [**Prior-data Fitted Networks (PFNs)**](https://www.automl.org/pfns) are a class of neural networks trained on **synthetic datasets sampled from a prior distribution** to directly approximate the **posterior predictive distribution (PPD)**. They enable **Bayesian prediction** through in-context learning and have been applied across tabular data, time series, Bayesian optimization, symbolic regression, and beyond.

### Example — TabPFN

A landmark success of the PFN framework is the **Tabular Foundation Model (TabPFN)** published in *Nature*: [Accurate predictions on small data with a tabular foundation model](https://www.nature.com/articles/s41586-024-08328-6). It showed that a TabPFN trained on over 100 million synthetic tabular tasks can outperform traditional ML models (like XGBoost, CatBoost, LightGBM) on a wide range of small real datasets.

- Key idea: Train a transformer on 100 million synthetic tabular tasks, enabling zero-training predictions on new datasets.  
- Architecture: An adapted transformer encoder designed for two-dimensional tabular data, supporting categorical + numeric features, missing values, and heterogeneous distributions.  
- Impact: TabPFN demonstrates that PFNs can act as *foundation models* for tabular data, achieving state-of-the-art accuracy on small-data benchmarks in seconds, outperforming conventional AutoML systems.

This work established PFNs as a new family of **foundation models for structured data**, analogous to LLMs for text.

<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-024-08328-6/MediaObjects/41586_2024_8328_Fig1_HTML.png" alt="Overview of TabPFN" width="650">
 
 Figure: Overview of TabPFN.

## Highlights

| Venue      | Title                                                        | Code                                        |
| :--------- | :----------------------------------------------------------- | :------------------------------------------ |
| ICLR 2023  | [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://arxiv.org/abs/2207.01848) | [Code](https://github.com/PriorLabs/TabPFN) |
| Nature     | [Accurate predictions on small data with a tabular foundation model](https://www.nature.com/articles/s41586-024-08328-6) | [Code](https://github.com/PriorLabs/TabPFN) |
| arXiv 2025 | [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://arxiv.org/abs/2511.08667) | [Code](https://github.com/PriorLabs/TabPFN) |
| arXiv 2026 | [TabPFN-3: Technical Report](https://arxiv.org/abs/2605.13986) | [Code](https://github.com/PriorLabs/TabPFN) |

**Please see the [Foundation Models for Structured Data Workshop](https://icml-structured-fm-workshop.github.io) for recent progress.**

## Foundations

*Foundations and theoretical insights into PFNs, amortized inference, and Bayesian learning.*

| Venue        | Title                                                        | Code                                                         |
| :----------- | :----------------------------------------------------------- | :----------------------------------------------------------- |
| ICLR 2022    | [Transformers Can Do Bayesian Inference](https://arxiv.org/abs/2112.10510) | [Code](https://github.com/automl/TransformersCanDoBayesianInference) |
| ICML 2023    | [Statistical Foundations of Prior-Data Fitted Networks](https://arxiv.org/abs/2305.11097) | [Code](https://gist.github.com/tnagler/62f6ce1f996333c799c81f1aef147e72) |
| NeurIPS 2023 | [Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection](https://arxiv.org/abs/2306.04637) | [Code](https://github.com/allenbai01/transformers-as-statisticians) |
| ICML 2024    | [Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective](https://arxiv.org/abs/2406.00793) | [Code](https://github.com/meta-inf/bayes_icl)                |
| ICML 2025    | [Can Transformers Learn Full Bayesian Inference in Context?](https://arxiv.org/abs/2501.16825) | [Code](https://github.com/ArikReuter/ICL_for_Full_Bayesian_Inference) |

## Papers

- [On Tabular Data](#on-tabular-data)
- [On Time Series](#on-time-series)
- [On Graphs](#on-graphs)
- [On Relational Databases](#on-relational-databases)
- [On Causal](#on-causal)
- [All Papers](#all-papers)

### On Tabular Data

| Venue        | Title                                                        | Code                                                         |
| :----------- | :----------------------------------------------------------- | :----------------------------------------------------------- |
| arXiv 2026   | [TabPFN-3: Technical Report](https://arxiv.org/abs/2605.13986) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| arXiv 2026   | [FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data](https://arxiv.org/abs/2603.16513) | —                                                            |
| ICML 2026    | [LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models](https://arxiv.org/abs/2606.04485) | [Code](https://github.com/limix-ldm-ai/LimiX)                |
| ICML 2026    | [TabICLv2: A better, faster, scalable, and open tabular foundation model](https://arxiv.org/abs/2602.11139) | [Code](https://github.com/soda-inria/tabicl)                 |
| WWW 2026     | [Tabular Foundation Models are Strong Graph Anomaly Detectors](https://arxiv.org/abs/2601.17301) | [Code](https://github.com/Cloudy1225/TFM4GAD)                |
| arXiv 2025   | [nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN](https://arxiv.org/abs/2511.03634) | [Code](https://github.com/automl/nanoTabPFN)                 |
| arXiv 2025   | [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://arxiv.org/abs/2511.08667) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| arXiv 2025   | [LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://arxiv.org/abs/2509.03505) | [Code](https://github.com/limix-ldm/LimiX)                   |
| arXiv 2025   | [On Finetuning Tabular Foundation Models](https://arxiv.org/abs/2506.08982) | [Code](https://github.com/yandex-research/tabpfn-finetuning) |
| NeurIPS 2025 | [Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models](https://www.amazon.science/blog/mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models) | [Code](https://huggingface.co/autogluon/mitra-classifier)    |
| NeurIPS 2025 | [Do-PFN: In-context Learning for Causal Effect Estimation](https://arxiv.org/abs/2506.06039) | [Code](https://github.com/jr2021/Do-PFN)                     |
| NeurIPS 2025 | [TabDPT: An Open Tabular Foundation Model](https://arxiv.org/abs/2410.18164) | [Code](https://github.com/layer6ai-labs/TabDPT-inference)    |
| NeurIPS 2025 | [A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities](https://arxiv.org/abs/2502.17361) | —                                                            |
| ICML 2025    | [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://arxiv.org/abs/2502.05564) | [Code](https://github.com/soda-inria/tabicl)                 |
| Nature 2025  | [Accurate predictions on small data with a tabular foundation model](https://www.nature.com/articles/s41586-024-08328-6) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| ICLR 2023    | [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://arxiv.org/abs/2207.01848) | [Code](https://github.com/PriorLabs/TabPFN)                  |

### On Time Series

| Venue                | Title                                                        | Code                                                    |
| :------------------- | :----------------------------------------------------------- | :------------------------------------------------------ |
| TSALM @ ICLR 2026    | [Interventional Time Series Priors for Causal Foundation Models](https://arxiv.org/abs/2603.11090) | [Code](https://github.com/thummd/CausalTimePrior)       |
| ICLR 2026            | [CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data](https://arxiv.org/abs/2508.02879) | [Code](https://github.com/ShifengXIE/CauKer)            |
| arXiv 2025           | [From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models](https://arxiv.org/abs/2501.02945) | [Code](https://github.com/PriorLabs/tabpfn-time-series) |
| FMSD @ ICML 2025     | [From Tabular to Time Series: Can TabPFN Handle Mixed Data? A Study on PhysioNet](https://openreview.net/forum?id=HVugmZyXbd) | —                                                       |
| FMSD @ ICML 2025     | [Explore the Time Series Forecasting Potential of TabPFN Leveraging the Intrinsic Periodicity of Data](https://openreview.net/forum?id=7JGD1kNlzU) | [Code](https://github.com/sibo-cai/TabPFN-TSP)          |
| AAAI 2025            | [TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data](https://arxiv.org/abs/2502.16294) | [Code](https://github.com/egetaga/TimePFN)              |
| arXiv 2024           | [LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting](https://arxiv.org/abs/2405.10093) | [Code](https://github.com/StijnVerdenius/Lat-PFN)       |
| TSALM @ NeurIPS 2024 | [Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models](https://openreview.net/forum?id=YBOQ5HnzI6) | [Code](https://github.com/automl/Mamba4Cast)            |
| NeurIPS 2023         | [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://arxiv.org/abs/2311.01933) | [Code](https://github.com/abacusai/ForecastPFN)         |

### On Graphs

| Venue      | Title                                                        | Code                                                |
| :--------- | :----------------------------------------------------------- | :-------------------------------------------------- |
| KDD 2026   | [Node4All: Learning Node Representation Beyond Datasets](https://github.com/dooho00/node4all/blob/main/docs/node4all_kdd_2026.pdf) | [Code](https://github.com/dooho00/node4all)         |
| arXiv 2026 | [KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning](https://arxiv.org/abs/2605.14907) | [Code](https://github.com/HKUST-KnowComp/KGPFN)     |
| ICML 2026  | [GraphPFN: A Prior-Data Fitted Graph Foundation Model](https://arxiv.org/abs/2509.21489) | [Code](https://github.com/yandex-research/graphpfn) |
| arXiv 2026 | [TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models](https://arxiv.org/abs/2602.08592) | —                                                   |
| WWW 2026   | [Tabular Foundation Models are Strong Graph Anomaly Detectors](https://arxiv.org/abs/2601.17301) | [Code](https://github.com/Cloudy1225/TFM4GAD)       |
| arXiv 2025 | [Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?](https://arxiv.org/pdf/2512.08798) | —                                                   |
| arXiv 2025 | [Turning Tabular Foundation Models into Graph Foundation Models](https://arxiv.org/abs/2508.20906) | [Code](https://github.com/yandex-research/G2T-FM)   |
| arXiv 2025 | [Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models](https://arxiv.org/abs/2509.07143) | [Code](https://github.com/ahayler/tag)              |
| arXiv 2025 | [LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://arxiv.org/abs/2509.03505) | [Code](https://github.com/limix-ldm/LimiX)          |

### On Relational Databases

### On Causal

| Venue        | Title                                                        | Code                                                   |
| :----------- | :----------------------------------------------------------- | :----------------------------------------------------- |
| ICML 2026    | [Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference](https://arxiv.org/abs/2603.12037) | [Code](https://github.com/Valentyn1997/freq-cons-pfns) |
| ICLR 2026    | [Foundation Models for Causal Inference via Prior-Data Fitted Networks](https://arxiv.org/abs/2506.10914) | [Code](https://github.com/yccm/CausalFM)               |
| arXiv 2025   | [Amortized Causal Discovery with Prior-Fitted Networks](https://arxiv.org/abs/2512.11840) | —                                                      |
| NeurIPS 2025 | [Do-PFN: In-context Learning for Causal Effect Estimation](https://arxiv.org/abs/2506.06039) | [Code](https://github.com/jr2021/Do-PFN)               |
| NeurIPS 2025 | [CausalPFN: Amortized Causal Effect Estimation via In-Context Learning](https://arxiv.org/abs/2506.07918) | [Code](https://github.com/vdblm/CausalPFN)             |
| ICML 2025    | [FairPFN: A Tabular Foundation Model for Causal Fairness](https://arxiv.org/abs/2506.07049) | [Code](https://github.com/jr2021/FairPFN)              |

### On AI4Science

### All Papers

| Venue                  | Title                                                        | Code                                                         |
| :--------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |
| FMSD @ ICML 2026 | [PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling](https://arxiv.org/abs/2607.23447) | — |
| FMSD @ ICML 2026 | [SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference](https://arxiv.org/abs/2605.15488) | [Code](https://github.com/rgklab/SurvivalPFN) |
| KDD 2026               | [StablePFN: Stable Prediction with Causal-Aware Tabular Foundation Model](https://dl.acm.org/doi/10.1145/3770855.3818030) | [Code](https://github.com/GuanZhengkang/StablePFN)           |
| KDD 2026               | [Node4All: Learning Node Representation Beyond Datasets](https://github.com/dooho00/node4all/blob/main/docs/node4all_kdd_2026.pdf) | [Code](https://github.com/dooho00/node4all)                  |
| KDD 2026               | [One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets](https://arxiv.org/abs/2606.15752) | [Code](https://zenodo.org/records/20403804)                  |
| KDD 2026               | [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://arxiv.org/abs/2502.16840) | —                                                            |
| arXiv 2026             | [TabPFN-3: Technical Report](https://arxiv.org/abs/2605.13986) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| arXiv 2026             | [OpenRFM: Dissecting Relational In-Context Learning](https://arxiv.org/abs/2606.04320) | —                                                            |
| arXiv 2026             | [VIP-COP: Context Optimization for Tabular Foundation Models](https://arxiv.org/abs/2605.12904) | —                                                            |
| arXiv 2026             | [Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning](https://arxiv.org/abs/2605.07799) | [Code](https://github.com/xyvivian/PIQL)                     |
| arXiv 2026             | [KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning](https://arxiv.org/abs/2605.14907) | [Code](https://github.com/HKUST-KnowComp/KGPFN)              |
| ICML 2026              | [No Need to Train Your RDB Foundation Model](https://arxiv.org/abs/2602.13697) | [Code](https://github.com/HKUSHXLab/rdblearn)                |
| ICML 2026              | [Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models](https://arxiv.org/abs/2605.06510) | [Code](https://github.com/amirbalef/is_one_layer_enough)     |
| ICML 2026              | [End-to-End Compression for Tabular Foundation Models](https://arxiv.org/abs/2602.05649) | [Code](https://github.com/machinelearningnuremberg/TACO)     |
| ICML 2026              | [LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models](https://arxiv.org/abs/2606.04485) | [Code](https://github.com/limix-ldm-ai/LimiX)                |
| ICML 2026              | [TabICLv2: A better, faster, scalable, and open tabular foundation model](https://arxiv.org/abs/2602.11139) | [Code](https://github.com/soda-inria/tabicl)                 |
| ICML 2026              | [TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention](https://arxiv.org/abs/2606.07345) | [Code](https://github.com/LAMDA-Tabular/TabSwift)            |
| ICML 2026              | [Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference](https://arxiv.org/abs/2603.12037) | [Code](https://github.com/Valentyn1997/freq-cons-pfns)       |
| ICML 2026              | [Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment](https://arxiv.org/abs/2605.04363) | [Code](https://github.com/seunghan96/DistPFN)                |
| ICML 2026              | [Relational In-Context Learning via Synthetic Pre-training with Structural Prior](https://arxiv.org/abs/2603.03805) | [Code](https://github.com/MuLabPKU/RDBPFN)                   |
| ICML 2026              | [PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models](https://arxiv.org/abs/2602.04029) | [Code](https://github.com/snap-stanford/plurel)              |
| ICML 2026              | [From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection](https://arxiv.org/abs/2602.03018) | [Code](https://github.com/psorus/Outformer)                  |
| ICML 2026              | [GraphPFN: A Prior-Data Fitted Graph Foundation Model](https://arxiv.org/abs/2509.21489) | [Code](https://github.com/yandex-research/graphpfn)          |
| ICML 2026              | [Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation](https://arxiv.org/abs/2502.02463) | [Code](https://github.com/GWhittle110/distribution-transformers) |
| ICML 2026              | [When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach](https://arxiv.org/abs/2605.19662) | —                                                            |
| arXiv 2026             | [Can Tabular Foundation Models Guide Exploration in Robot Policy Learning](https://arxiv.org/abs/2604.27667) | —                                                            |
| CVPR 2026              | [MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning](https://arxiv.org/abs/2602.20223) | [Code](https://github.com/too-z/MultiModalPFN)               |
| arXiv 2026             | [FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data](https://arxiv.org/abs/2603.16513) | —                                                            |
| arXiv 2026             | [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://arxiv.org/abs/2603.17570) | [Code](https://github.com/psorus/FoMo-X)                     |
| arXiv 2026             | [Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure](https://arxiv.org/abs/2603.10254) | [Code](https://github.com/DavideTugnoli/tabpfn-causal-synthetic) |
| arXiv 2026             | [Amortized Spectral Kernel Discovery via Prior-Data Fitted Network](https://arxiv.org/abs/2601.21731) | —                                                            |
| arXiv 2026             | [TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering](https://arxiv.org/abs/2601.21656) | [Code](https://github.com/Tianqi-Zhao/TabClustPFN)           |
| arXiv 2026             | [MapPFN: Learning Causal Perturbation Maps in Context](https://github.com/marvinsxtr/MapPFN) | [Code](https://github.com/marvinsxtr/MapPFN)                 |
| TSALM @ ICLR 2026      | [Interventional Time Series Priors for Causal Foundation Models](https://arxiv.org/abs/2603.11090) | [Code](https://github.com/thummd/CausalTimePrior)            |
| arXiv 2026             | [Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables](https://arxiv.org/abs/2603.15802) | —                                                            |
| arXiv 2026             | [TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models](https://arxiv.org/abs/2602.08592) | —                                                            |
| ICLR 2026              | [CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data](https://arxiv.org/abs/2508.02879) | [Code](https://github.com/ShifengXIE/CauKer)                 |
| ICLR 2026              | [Foundation Models for Causal Inference via Prior-Data Fitted Networks](https://arxiv.org/abs/2506.10914) | [Code](https://github.com/yccm/CausalFM)                     |
| ICLR 2026              | [GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models](https://openreview.net/forum?id=9iTdKS4SRQ) | [Code](https://github.com/deepbiolab/gitbo)                  |
| ICLR 2026              | [Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors](https://openreview.net/forum?id=FmxRzlu0rT) | [Code](https://sites.google.com/view/nodepfn)                |
| ICLR 2026              | [Using maximal information auxiliary variables to improve synthetic data generation based on TabPFN foundation models](https://openreview.net/forum?id=6PkiUAcTWF) | —                                                            |
| ICLR 2026              | [Efficient Autoregressive Inference for Transformer Probabilistic Models](https://arxiv.org/abs/2510.09477) | [Code](https://github.com/acerbilab/transformer-ar-buffer)   |
| WWW 2026               | [Tabular Foundation Models are Strong Graph Anomaly Detectors](https://arxiv.org/abs/2601.17301) | [Code](https://github.com/Cloudy1225/TFM4GAD)                |
| WWW 2026               | [Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN](https://arxiv.org/abs/2601.17912) | [Code](https://github.com/ql909/An-Empirical-Study-of-TabPFN) |
| WWW 2026               | [Exploring Fine-Tuning for Tabular Foundation Models](https://arxiv.org/abs/2601.09654) | —                                                            |
| WWW 2026               | [Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning](https://arxiv.org/abs/2512.00181) | [Code](https://github.com/Lexsi-Labs/Orion-BiX)              |
| arXiv 2025             | [Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?](https://arxiv.org/pdf/2512.08798) | —                                                            |
| arXiv 2025             | [nanoTabPFN: A Lightweight and Educational Reimplementation of TabPFN](https://arxiv.org/abs/2511.03634) | [Code](https://github.com/automl/nanoTabPFN)                 |
| arXiv 2025             | [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://arxiv.org/abs/2511.08667) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| arXiv 2025             | [Amortized Causal Discovery with Prior-Fitted Networks](https://arxiv.org/abs/2512.11840) | —                                                            |
| arXiv 2025             | [Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning](https://arxiv.org/abs/2511.02818) | [Code](https://github.com/Lexsi-Labs/Orion-MSP)              |
| arXiv 2025             | [Transformers Can Do Bayesian Clustering](https://arxiv.org/abs/2510.24318) | [Code](https://github.com/pbhaskaran/Cluster-PFN)            |
| OpenReview             | [PDE-PFN: Prior-Data Fitted Neural PDE Solver](https://openreview.net/forum?id=z7ilspv4uH) | —                                                            |
| OpenReview             | [Task-Aligned Attention Retrieval for Scaling Tabular Foundation Models](https://openreview.net/forum?id=qBMNsGiE3u) | —                                                            |
| OpenReview             | [Large-Scale Pretraining Offers Modest Benefits for Tabular Transfer Learning](https://openreview.net/forum?id=G5zJaSxMGN) | —                                                            |
| OpenReview             | [RaBEL: Scale-Aware Radial-Basis Embeddings for Tabular Foundation Models](https://openreview.net/forum?id=odoTDh3QUk) | —                                                            |
| arXiv 2025             | [TabPFN: One Model to Rule Them All?](https://arxiv.org/abs/2505.20003) | [Code](https://github.com/qinglong-tian/tabpfn_study)        |
| arXiv 2025             | [TabImpute: Accurate and Fast Zero-Shot Missing-Data Imputation with a Pre-Trained Transformer](https://arxiv.org/abs/2510.02625) | [Code](https://github.com/jacobf18/tabular)                  |
| arXiv 2025             | [Decoupled-Value Attention for Prior-Data Fitted Networks: GP Inference for Physical Equations](https://arxiv.org/abs/2509.20950) | [Code](https://github.com/PSquare-Lab/DVA-PFN)               |
| arXiv 2025             | [Turning Tabular Foundation Models into Graph Foundation Models](https://arxiv.org/abs/2508.20906) | [Code](https://github.com/yandex-research/G2T-FM)            |
| arXiv 2025             | [Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models](https://arxiv.org/abs/2509.07143) | [Code](https://github.com/ahayler/tag)                       |
| arXiv 2025             | [LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence](https://arxiv.org/abs/2509.03505) | [Code](https://github.com/limix-ldm/LimiX)                   |
| arXiv 2025             | [Gradient Free Deep Reinforcement Learning With TabPFN](https://arxiv.org/abs/2509.11259) | —                                                            |
| arXiv 2025             | [Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning](https://arxiv.org/abs/2507.20369) | —                                                            |
| arXiv 2025             | [On Finetuning Tabular Foundation Models](https://arxiv.org/abs/2506.08982) | [Code](https://github.com/yandex-research/tabpfn-finetuning) |
| arXiv 2025             | [TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts](https://arxiv.org/abs/2510.06162) | [Code](https://github.com/pfeiferAI/TabPFN-Wide)             |
| arXiv 2025             | [Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data](https://arxiv.org/abs/2509.00326) | [Code](https://github.com/mrsergazinov/chunk_tabpfn)         |
| arXiv 2025             | [From Tables to Time: How TabPFN-v2 Outperforms Specialized Time Series Forecasting Models](https://arxiv.org/abs/2501.02945) | [Code](https://github.com/PriorLabs/tabpfn-time-series)      |
| arXiv 2025             | [Realistic Evaluation of TabPFN v2 in Open Environments](https://arxiv.org/abs/2505.16226) | [Code](https://anonymous.4open.science/r/tabpfn-ood-4E65)    |
| SSRN 2025              | [MultiTabPFN: Codebook-based Extensions of TabPFN for High-Class-Count Tabular Classification](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5545797) | —                                                            |
| TCBBIO 2025            | [GPFN: Prior-Data Fitted Networks for Genomic Prediction](https://www.biorxiv.org/content/10.1101/2023.09.20.558648) | [Code](https://github.com/jubbens/gpfn)                      |
| RML @ NeurIPS 2025     | [Robust Multi-task Modeling for Bayesian Optimization via In-Context Learning](https://openreview.net/forum?id=iwqJLEPgvF) | —                                                            |
| NeurIPS 2025           | [Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models](https://www.amazon.science/blog/mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models) | [Code](https://huggingface.co/autogluon/mitra-classifier)    |
| NeurIPS 2025           | [Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models](https://arxiv.org/abs/2504.17660) | [Code](https://github.com/mackelab/npe-pfn)                  |
| NeurIPS 2025           | [Do-PFN: In-context Learning for Causal Effect Estimation](https://arxiv.org/abs/2506.06039) | [Code](https://github.com/jr2021/Do-PFN)                     |
| NeurIPS 2025           | [ConTextTab: A Semantics-Aware Tabular In-Context Learner](https://arxiv.org/abs/2506.10707) | [Code](https://github.com/SAP-samples/contexttab)            |
| NeurIPS 2025           | [ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data](https://arxiv.org/abs/2505.10704) | [Code](https://github.com/gmum/zeus)                         |
| NeurIPS 2025           | [TabDPT: An Open Tabular Foundation Model](https://arxiv.org/abs/2410.18164) | [Code](https://github.com/layer6ai-labs/TabDPT-inference)    |
| NeurIPS 2025           | [CausalPFN: Amortized Causal Effect Estimation via In-Context Learning](https://arxiv.org/abs/2506.07918) | [Code](https://github.com/vdblm/CausalPFN)                   |
| NeurIPS 2025           | [A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities](https://arxiv.org/abs/2502.17361) | —                                                            |
| NeurIPS 2025           | [EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks](https://arxiv.org/abs/2502.06684) | [Code](https://github.com/geoalgo/equitabpfn)                |
| ECAI 2025              | [In-Context Decision Making for Optimizing Complex AutoML Pipelines](https://arxiv.org/abs/2508.13657) | [Code](https://github.com/amirbalef/CASHPlus)                |
| FMSD @ ICML 2025       | [State-Space Models for Tabular Prior-Data Fitted Networks](https://arxiv.org/abs/2510.14573) | [Code](https://github.com/felixmkoch/Structured-State-Space-Models-for-PFNs) |
| FMSD @ ICML 2025       | [Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data](https://arxiv.org/abs/2507.03971) | —                                                            |
| FMSD @ ICML 2025       | [From Tabular to Time Series: Can TabPFN Handle Mixed Data? A Study on PhysioNet](https://openreview.net/forum?id=HVugmZyXbd) | —                                                            |
| FMSD @ ICML 2025       | [Early Stopping Tabular In-Context Learning](https://arxiv.org/abs/2506.21387) | —                                                            |
| FMSD @ ICML 2025       | [Explore the Time Series Forecasting Potential of TabPFN Leveraging the Intrinsic Periodicity of Data](https://openreview.net/forum?id=7JGD1kNlzU) | [Code](https://github.com/sibo-cai/TabPFN-TSP)               |
| ICML 2025              | [Position: The Future of Bayesian Prediction Is Prior-Fitted](https://arxiv.org/abs/2505.23947) | —                                                            |
| ICML 2025              | [Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks](https://arxiv.org/abs/2505.23032) | [Code]([DongWooLee-Eli/nslpfn](https://github.com/DongWooLee-Eli/nslpfn)) |
| ICML 2025              | [Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer](https://arxiv.org/abs/2502.04573) | [Code](https://github.com/yulun-rayn/APT)                    |
| ICML 2025              | [TabPFN-Unleashed: A Scalable and Effective Solution to Tabular Prediction](https://proceedings.mlr.press/v267/liu25cn.html) | [Code](https://github.com/LAMDA-Tabular/BETA)                |
| ICML 2025              | [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://arxiv.org/abs/2502.05564) | [Code](https://github.com/soda-inria/tabicl)                 |
| ICML 2025              | [TabFlex: Scaling Tabular Learning to Millions with Linear Attention](https://arxiv.org/abs/2506.05584v1) | [Code](https://github.com/tuanqdinh/ICML25_TabFlex)          |
| ICML 2025              | [FairPFN: A Tabular Foundation Model for Causal Fairness](https://arxiv.org/abs/2506.07049) | [Code](https://github.com/jr2021/FairPFN)                    |
| ICML 2025              | [Can Transformers Learn Full Bayesian Inference in Context?](https://arxiv.org/abs/2501.16825) | [Code](https://github.com/ArikReuter/ICL_for_Full_Bayesian_Inference) |
| AISTATS 2025           | [Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners](https://arxiv.org/abs/2503.01256) | [Code](https://github.com/yxzwang/BoostPFN)                  |
| TMLR 2025              | [FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection](https://arxiv.org/abs/2409.05672) | [Code](https://github.com/A-Chicharito-S/FoMo-0D)            |
| AABI @ ICLR 2025       | [Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors](https://arxiv.org/abs/2505.11325) | —                                                            |
| FPI @ ICLR 2025        | [α-PFN: In-Context Learning Entropy Search](https://openreview.net/forum?id=IMVqPGYxyD) | —                                                            |
| ICLR 2025              | [Mixture of In-Context Prompters for Tabular PFNs](https://arxiv.org/abs/2405.16156) | —                                                            |
| ICLR 2025              | [KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks](https://openreview.net/forum?id=E1m5yGMOiV) | [Code](https://github.com/automl/KinPFN)                     |
| AAAI 2025              | [TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data](https://arxiv.org/abs/2502.16294) | [Code](https://github.com/egetaga/TimePFN)                   |
| ICLR 2025              | [MotherNet: Fast Training and Inference via Hyper-Network Transformers](https://arxiv.org/abs/2312.08598) | [Code](https://github.com/microsoft/ticl)                    |
| Nature 2025            | [Accurate predictions on small data with a tabular foundation model](https://www.nature.com/articles/s41586-024-08328-6) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| OpenReview             | [Attic: A New Architecture for Tabular In-Context Learning Transformers](https://openreview.net/forum?id=DSl9sSuUhp) | [Code](https://openreview.net/attachment?id=DSl9sSuUhp&name=supplementary_material) |
| arXiv 2024             | [LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting](https://arxiv.org/abs/2405.10093) | [Code](https://github.com/StijnVerdenius/Lat-PFN)            |
| arXiv 2024             | [Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers](https://arxiv.org/abs/2405.13396) | [Code](https://openreview.net/attachment?id=pE0UM18TQh&name=supplementary_material) |
| arXiv 2024             | [Tokenize Features, Enhancing Tables: The FT-TabPFN Model for Tabular Classification](https://arxiv.org/abs/2406.06891) | [Code](https://github.com/ds-brx/seminar-LLMTab-FTtabpfn)    |
| xAI 2024               | [Interpretable Machine Learning for TabPFN](https://arxiv.org/abs/2403.10923) | [Code](https://github.com/david-rundel/tabpfn_iml)           |
| TSALM @ NeurIPS 2024   | [Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models](https://openreview.net/forum?id=YBOQ5HnzI6) | [Code](https://github.com/automl/Mamba4Cast)                 |
| TRL @ NeurIPS 2024     | [GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning](https://openreview.net/forum?id=5Taa8ZaZ5o) | —                                                            |
| TRL @ NeurIPS 2024     | [Adapting TabPFN for Zero-Inflated Metagenomic Data](https://openreview.net/forum?id=3I0bVvUj25#discussion) | —                                                            |
| TRL @ NeurIPS 2024     | [Towards Localization via Data Embedding for TabPFN](https://openreview.net/forum?id=LFyQyV5HxQ) | —                                                            |
| TRL @ NeurIPS 2024     | [Exploration of autoregressive models for in-context learning on tabular data](https://openreview.net/forum?id=4dOJ0PRY7R) | —                                                            |
| NeurIPS 2024           | [TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks](https://arxiv.org/abs/2402.11137) | [Code](https://github.com/penfever/TuneTables)               |
| NeurIPS 2024           | [Retrieval & Fine-Tuning for In-Context Tabular Models](https://proceedings.nips.cc/paper_files/paper/2024/hash/c40daf14d7a6469e65116507c21faeb7-Abstract-Conference.html) | [Code](https://github.com/layer6ai-labs/LoCalPFN)            |
| NeurIPS 2024           | [Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data](https://arxiv.org/abs/2411.10634) | [Code](https://github.com/automl/Drift-Resilient_TabPFN)     |
| NeurIPS 2024           | [TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models](https://arxiv.org/abs/2409.16118) | [Code](https://github.com/andreimargeloiu/TabEBM)            |
| ICL @ ICML 2024        | [TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting](https://arxiv.org/abs/2406.01805) | [Code](https://github.com/AdrianBZG/TabMDA)                  |
| ICML 2024              | [In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization](https://arxiv.org/abs/2404.16795) | [Code](https://github.com/automl/ifBO)                       |
| ME-FoMo @ ICLR 2024    | [In-Context Data Distillation with TabPFN](https://arxiv.org/abs/2402.06971) | —                                                            |
| Blogposts  @ ICLR 2024 | [What exactly has TabPFN learned to do?](https://arxiv.org/abs/2502.08978) | [Code](https://github.com/calvinmccarter/tabpfn-eval)        |
| TRL @ NeurIPS 2023     | [Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks](https://arxiv.org/abs/2311.10609) | [Code](https://github.com/penfever/TuneTables)               |
| TRL @ NeurIPS 2023     | [Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning](https://arxiv.org/abs/2311.07343) | —                                                            |
| TRL @ NeurIPS 2023     | [TabPFGen -- Tabular Data Generation with TabPFN](https://arxiv.org/abs/2406.05216) | [Code](https://github.com/sebhaan/TabPFGen)                  |
| NeurIPS 2023           | [Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks](https://arxiv.org/abs/2310.20447) | [Code](https://github.com/automl/lcpfn)                      |
| NeurIPS 2023           | [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://arxiv.org/abs/2311.01933) | [Code](https://github.com/abacusai/ForecastPFN)              |
| ICML 2023              | [PFNs4BO: In-Context Learning for Bayesian Optimization](https://arxiv.org/abs/2305.17535) | [Code](https://github.com/automl/PFNs4BO)                    |
| ICML 2023              | [Statistical Foundations of Prior-Data Fitted Networks](https://arxiv.org/abs/2305.11097) | [Code](https://gist.github.com/tnagler/62f6ce1f996333c799c81f1aef147e72) |
| ICLR 2023              | [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://arxiv.org/abs/2207.01848) | [Code](https://github.com/PriorLabs/TabPFN)                  |
| ICLR 2022              | [Transformers Can Do Bayesian Inference](https://arxiv.org/abs/2112.10510) | [Code](https://github.com/automl/TransformersCanDoBayesianInference) |

## GitHub Repositories

| Repository                                                   | Description                                                  |
| :----------------------------------------------------------- | :----------------------------------------------------------- |
| **[`automl/PFNs`](https://github.com/automl/PFNs)**          | Canonical PFN implementation; synthetic task generation, Bayesian inference via transformers. |
| **[`PriorLabs/TabPFN`](https://github.com/PriorLabs/TabPFN)** | Official Tabular PFN implementation (classification + regression) |
| **[`automl/nanoTabPFN`](https://github.com/automl/nanoTabPFN)** | Lightweight and educational reimplementation of TabPFN       |
| **[`PriorLabs/tabpfn-extensions`](https://github.com/PriorLabs/tabpfn-extensions)** | Extensions: interpretability, more classes, imputation, and analysis tools |
| **[`PriorLabs/awesome-tabpfn`](https://github.com/PriorLabs/awesome-tabpfn)** | Community-curated list of TabPFN applications and papers     |
| **[`PriorLabs/tabpfn-time-series`](https://github.com/PriorLabs/tabpfn-time-series)** | Time-series adaptation of TabPFN                             |
| **[`david-rundel/tabpfn_iml`](https://github.com/david-rundel/tabpfn_iml)** | Interpretability module for TabPFN (SHAP, feature attribution) |
| **[`abacusai/ForecastPFN`](https://github.com/abacusai/ForecastPFN)** | PFN for zero-shot time-series forecasting                    |
| **[`soda-inria/tabicl`](https://github.com/soda-inria/tabicl)** | a more scalable tabular foundation model                     |
| **[`limix-ldm/LimiX`](https://github.com/limix-ldm/LimiX)**  | LimiX: a tabular foundation model generalizing TabPFN        |
| **[`autogluon/tabrepo`](https://github.com/autogluon/tabrepo)** | Living benchmark for tabular ML; includes foundation-model baselines |
| **[`autogluon/autogluon`](https://github.com/autogluon/autogluon)** | End-to-end AutoML framework supporting tabular, time-series, and multimodal data; often used as benchmark in PFN/TabPFN work |

## Other Applications

1. [Predicting Early Outcomes of Prostatic Artery Embolization Using n-Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study](https://www.mdpi.com/2075-4418/15/11/1351)
2. [Virtual Screening of Natural Anti-Senescent Compounds Based on Sq-TabPFN](https://ieeexplore.ieee.org/document/10864333)
3. [From Rows to Yields: How Foundation Models for Tabular Data Simplify Crop Yield Prediction](https://arxiv.org/abs/2506.19046)
4. [Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data](https://arxiv.org/abs/2509.10048)
5. [Early Fault Classification in Rotating Machinery With Limited Data Using TabPFN](https://ieeexplore.ieee.org/document/10318062)
6. [Improved Ethereum Fraud Detection Mechanism with Explainable Tabular Transformer Model](https://ieeexplore.ieee.org/document/10835625)
7. [Fast and Accurate Zero-Training Classification for Tabular Engineering Data](https://arxiv.org/abs/2401.06948)
8. [A Fast and Reliable Transformer-Based TabPFN Model for Liver Disease Diagnosis](https://www.cureusjournals.com/articles/4072-a-fast-and-reliable-transformer-based-tabpfn-model-for-liver-disease-diagnosis#!/)
9. [Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis](https://pubmed.ncbi.nlm.nih.gov/39350610/)
10. [Explainable Classification for Non-Small Cell Lung Cancer Based on Positron Emission Tomography Features and Clinical Data](https://ieeexplore.ieee.org/abstract/document/10345893)
11. [Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring](https://arxiv.org/abs/2501.10677)
12. [Fault Diagnosis of Slewing Bearing Using Audible Sound Signal Based on Time Generative Adversarial Network–TabPFN Method](https://doi.org/10.1115/1.4068223)
13. [A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy](https://pubmed.ncbi.nlm.nih.gov/39113482/)
14. [Foundation Models for Cybersecurity: A Comprehensive Multi-Modal Evaluation of TabPFN and TabICL for Tabular Intrusion Detection](https://www.mdpi.com/2079-9292/14/19/3792)
15. [TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID](https://www.medrxiv.org/content/10.1101/2025.10.02.25337138)
16. [Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models](https://arxiv.org/abs/2509.11449)
17. [Tabular prior-data fitted network for urban air temperature inference and high temperature risk assessment](https://www.sciencedirect.com/science/article/abs/pii/S2210670725003609)
18. [Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping](https://arxiv.org/abs/2509.09408)
19. [Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data](https://arxiv.org/abs/2509.10048)
20. [Tabular foundation model for GEOAI benchmark problems BM/AirportSoilProperties/2/2025](https://arxiv.org/abs/2509.03191)

## Contributing

Contributions are welcome!

- Add papers, repos, or tutorials.
- Fix broken links.  
- Suggest new categories.

## License & Credits

This list aggregates publicly available PFN-related resources. Each work retains its own license and citation requirements. If you use these resources, please cite the corresponding paper and repository.

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