MaskGIT: Masked Generative Image Transformer
Open accessAbstract
Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster scan ordering (i.e. line-by-line). We find this strategy neither optimal nor efficient. This paper proposes a novel image synthesis paradigm using a bidirectional transformer decoder, which we term MaskGIT. During training, MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation. Our experiments demonstrate that MaskGIT significantly outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 48x. Besides, we illustrate that MaskGIT can be easily extended to various image editing tasks, such as inpainting, extrapolation, and image manipulation. Project page: masked-generative-image-transformer.github.io.
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Discrete tokenizers are deployed in various visual understanding, image generation, and video generation tasks.
“Thus, discrete tokenizers are deployed in various visual understanding (Sun et al., 2024; Team, 2024; Wang et al., 2024; Wu et al., 2024) as well as image (Chang et al., 2022; Esser et al., 2021; Ramesh et al., 2021; Sun et al., 2024; Yu et al., 2022) and video generation tasks (Ge et al., 2022; Hong et al., 2023; Kondratyuk et al., 2024; Luo et al., 2024; Villegas et al., 2023; Wu et al., 2022;”
From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Chang et al., 2022
Recent advances in deep learning have facilitated significant progress for various data modalities such as images, audio, and text.
“Recent advances in deep learning have facilitated significant progress for various data modalities [29,161,205], such as images [38,239], audio [110,170], and text [14,20,75,159,162].”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [29]
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Zhang, Q., Song, J., Huang, X., Chen, Y., & Liu, M.-Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00982 · Cited by 63 · see citing works
Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models
Dong, W., Xue, S., Duan, X., & Han, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00683 · Cited by 61 · see citing works
Learning Imbalanced Data with Vision Transformers
Xu, Z., Liu, R., Yang, S., Chai, Z., & Yuan, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01516 · Cited by 62 · see citing works
T-former: An Efficient Transformer for Image Inpainting
Deng, Y., Hui, S., Zhou, S., Meng, D., & Wang, J. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548446 · Cited by 59 · see citing works
Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
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DiffiT: Diffusion Vision Transformers for Image Generation
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Zero-shot spatial layout conditioning for text-to-image diffusion models
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HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models
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NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation
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Learning Compact Representations for LiDAR Completion and Generation
Xiong, Y., Ma, W.-C., Wang, J., & Urtasun, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00110 · Cited by 53 · see citing works
CityDreamer: Compositional Generative Model of Unbounded 3D Cities
Xie, H., Chen, Z., Hong, F., & Liu, Z. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00923 · Cited by 56 · see citing works
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Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T. P., & Willcocks, C. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_11 · Cited by 54 · see citing works
Explainable time series anomaly detection using masked latent generative modeling
Lee, D.-S., Malacarne, S., & Aune, E. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110826 · Cited by 51 · see citing works
Zheng, C., & Vedaldi, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02084 · Cited by 46 · see citing works
Text2Performer: Text-Driven Human Video Generation
Jiang, Y., Yang, S., Koh, T. L., Wu, W., Loy, C. C., & Liu, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02079 · Cited by 41 · see citing works
Coarse-to-Fine Amodal Segmentation with Shape Prior
Gao, J., Qian, X., Wang, Y., Xiao, T., He, T., Zhang, Z., & Fu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00122 · Cited by 41 · see citing works
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Sanghi, A., Fu, R., Liu, V., Willis, K. D. D., Shayani, H., Khasahmadi, A. H., Sridhar, S., & Ritchie, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01759 · Cited by 40 · see citing works
Masked Image Modeling: A Survey
Hondru, V., Croitoru, F.-A., Minaee, S., Ionescu, R. T., & Sebe, N. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-025-02524-1 · Cited by 65 · see citing works
RealFill: Reference-Driven Generation for Authentic Image Completion
Tang, L., Ruiz, N., Chu, Q., Li, Y., Holynski, A., Jacobs, D. E., Hariharan, B., Pritch, Y., Wadhwa, N., Aberman, K., et al. (2024) · ACM Transactions on Graphics · doi:10.1145/3658237 · Cited by 42 · see citing works
Latent diffusion model for conditional reservoir facies generation
Lee, D.-S., Ovanger, O., Eidsvik, J., Aune, E., Skauvold, J., & Hauge, R. (2024) · Computers & Geosciences · doi:10.1016/j.cageo.2024.105750 · Cited by 41 · see citing works
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
Hamamcı, İ. E., Er, S., Sekuboyina, A., Simsar, E., Tezcan, A., Simsek, A. G., Esirgün, S. N., Almas, F., Doğan, I., Dasdelen, M. F., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72986-7_8 · Cited by 36 · see citing works
Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review
Shi, Y., Jiang, K., Li, J., Qian, Z., Wen, J., Yang, M., Wang, K., & Yang, D. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3495045 · Cited by 38 · see citing works
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression
Chen, Y.-H., Xie, H.-S., Chen, C., Gao, Z.-L., Benjak, M., Peng, W.-H., & Östermann, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3427426 · Cited by 34 · see citing works
Diffusion Models Without Attention
Yan, J. N., Gu, J., & Rush, A. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00787 · Cited by 37 · see citing works
A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition
Ivanko, D., Ryumin, D., & Karpov, A. A. (2023) · Mathematics · doi:10.3390/math11122665 · Cited by 35 · see citing works
BAMM: Bidirectional Autoregressive Motion Model
Pinyoanuntapong, E., Saleem, M. U., Wang, P., Lee, M., Das, S., & Chen, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72633-0_10 · Cited by 32 · see citing works
Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder
Lin, X., Li, Y., Hsiao, J.-H., Ho, C., & Kong, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00173 · Cited by 33 · see citing works
ME- ViT: A Single-Load Memory-Efficient FPGA Accelerator for Vision Transformers
Marino, K., Zhang, P., & Prasanna, V. K. (2023) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc58850.2023.00039 · Cited by 34 · see citing works
MaskSketch: Unpaired Structure-guided Masked Image Generation
Bashkirova, D., Lezama, J., Sohn, K., Saenko, K., & Essa, I. A. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00187 · Cited by 32 · see citing works
Huang, M., Mao, Z., Chen, Z., & Zhang, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02164 · Cited by 30 · see citing works
Vlogger: Make Your Dream A Vlog
Zhuang, S., Li, K., Chen, X., Wang, Y., Liu, Z., Qiao, Y., & Wang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00841 · Cited by 32 · see citing works
Improved Masked Image Generation with Token-Critic
Lezama, J., Chang, H., Jiang, L., & Essa, I. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20050-2_5 · Cited by 27 · see citing works
Self-citationNÜWA-LIP: Language-guided Image Inpainting with Defect-free VQGAN
Ni, M., Li, X., & Zuo, W. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01363 · Cited by 27 · see citing works
Towards Variable and Coordinated Holistic Co-Speech Motion Generation
Liu, Y., Cao, Q., Wen, Y., Jiang, H., & Ding, C. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00155 · Cited by 26 · see citing works
Meng, Z., Xie, Y., Peng, X., Han, Z., & Jiang, H. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02594 · Cited by 26 · see citing works
Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers
Sun, Y., Zhou, H., Wang, K., Wu, Q., Hong, Z., Liu, J., Ding, E., Wang, J., Liu, Z., & Koike, H. (2022) · SIGGRAPH Asia Conference Papers · doi:10.1145/3550469.3555393 · Cited by 26 · see citing works
FontTransformer: Few-shot high-resolution Chinese glyph image synthesis via stacked transformers
Liu, Y., & Lian, Z. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109593 · Cited by 26 · see citing works
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation
Huang, M., Mao, Z., Wang, Q., & Zhang, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00199 · Cited by 25 · see citing works
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Wu, J., Gao, B., Gao, J., Yu, J., Chu, H., Yu, Q., Gong, X., Chang, Y. M., Tseng, H. E., Chen, H., et al. (2024) · Research · doi:10.34133/research.0399 · Cited by 24 · see citing works
MoVideo: Motion-Aware Video Generation with Diffusion Model
Liang, J., Fan, Y., Zhang, K., Timofte, R., Gool, L. V., & Ranjan, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72784-9_4 · Cited by 24 · see citing works
Finger Recovery Transformer: Toward Better Incomplete Fingerprint Identification
Jia, Z., Huang, C., Wang, Z., Fei, H., Wu, S. J., & Feng, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3419690 · Cited by 23 · see citing works
Edge Enhanced Image Style Transfer via Transformers
Zhang, C., Dai, Z., Cao, P.-X., & Yang, J. (2023) · International Conference on Multimedia Retrieval · doi:10.1145/3591106.3592257 · Cited by 20 · see citing works
Extreme Image Compression Using Fine-tuned VQGANs
Mao, Q., Yang, T., Zhang, Y., Wang, Z., Wang, M., Wang, S., Jin, L., & Ma, S. (2024) · DCC · doi:10.1109/dcc58796.2024.00028 · Cited by 21 · see citing works
Wang, Z., Wang, J., Liu, Z., & Qiu, Q. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02162 · Cited by 20 · see citing works
Architectural Synergies in Bi-Modal and Bi-Contrastive Learning
Gu, Y., Liu, B., Zhang, T., Sha, X., & Chen, S. (2024) · IEEE Access · doi:10.1109/access.2024.3457586 · Cited by 19 · see citing works
Ma, Y., Ye, W., Cui, C., Zhang, H., Xing, S., Ke, F., Wang, J., Miao, C., Chen, J., Rezatofighi, H., et al. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00114 · Cited by 19 · see citing works
Self-citationGINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Shen, B., Yan, X., Qi, C. R., Najibi, M., Deng, B., Guibas, L. J., Zhou, Y., & Anguelov, D. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00476 · Cited by 19 · see citing works
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting
Chen, H., & Zhao, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00725 · Cited by 19 · see citing works
Zheng, Q. S., Teng, P., Zhang, K., Ding, G., Lai, X., Li, Z., & Yuan, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112915 · Cited by 19 · see citing works
Li, X., Sun, Y., Lin, J., Li, L., Feng, T., & Yin, S. (2025) · Artificial Intelligence Science and Engineering · doi:10.23919/aise.2005.000007 · Cited by 18 · see citing works
Joint Holistic and Masked Face Recognition
Zhu, Y., Ren, M., Jing, H., Dai, L., Sun, Z., & Li, P. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3280717 · Cited by 19 · see citing works
UniFaRN: Unified Transformer for Facial Reaction Generation
Liang, C., Wang, J., Zhang, H., Tang, B., Huang, J., Wang, S., & Chen, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612854 · Cited by 18 · see citing works
Self-citationMasked Generative Video-to-Audio Transformers with Enhanced Synchronicity
Pascual, S., Yeh, C., Tsiamas, I., & Serrà, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73021-4_15 · Cited by 18 · see citing works
Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy
Mai, W., Zhang, J., Fang, P., & Zhang, Z. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3516698 · Cited by 21 · see citing works
Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks
Zhou, M., Wagner, M. W., Tabori, U. Y., Hawkins, C. E., Ertl-Wagner, B. B., & Khalvati, F. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109502 · Cited by 22 · see citing works
M2T: Masking Transformers Twice for Faster Decoding
Mentzer, F., Agustson, E., & Tschannen, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00492 · Cited by 17 · see citing works
SD-DiT: Unleashing the Power of Self-Supervised Discrimination in Diffusion Transformer *
Zhu, R., Pan, Y., Li, Y., Yao, T., Sun, Z., Mei, T., & Chen, C. W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00806 · Cited by 20 · see citing works
TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Dupont, E., Cherenkova, K., Mallis, D., Gusev, G., Kacem, A., & Aouada, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73030-6_2 · Cited by 17 · see citing works
Recent Advances in Discrete Speech Tokens: A Review
Guo, Y., Li, Z., Wang, H., Li, B., Shao, C., Zhang, H., Du, C., Xie, C., Liu, S., & Yu, K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3643619 · Cited by 20 · see citing works
Self-citationMOSO: Decomposing MOtion, Scene and Object for Video Prediction
Sun, M., Wang, N., Zhu, X., & Liu, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01796 · Cited by 17 · see citing works
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers
Cao, S., Yin, Y., Huang, L., Liu, Y., Zhao, X., Zhao, D., & Huang, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00677 · Cited by 16 · see citing works
Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency
Dai, J., Qin, X., Wang, S., Xu, L., Niu, K., & Zhang, P. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.005.2300574 · Cited by 16 · see citing works
Solving Motion Planning Tasks with a Scalable Generative Model
Hu, Y., Chai, S., Yang, Z., Qian, J., Li, K., Shao, W., Zhang, H., Xu, W., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_22 · Cited by 17 · see citing works
Self-citationLaMD: Latent Motion Diffusion for Image-Conditional Video Generation
Hu, Y., Chen, Z., & Luo, C. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02386-7 · Cited by 24 · see citing works
MapPrior: Bird’s-Eye View Map Layout Estimation with Generative Models
Zhu, X., Zyrianov, V., Liu, Z., & Wang, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00756 · Cited by 15 · see citing works
Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation
Lu, Z., Hu, K., Wang, C., Bai, L., & Wang, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i13.29332 · Cited by 15 · see citing works
MaskPLAN: Masked Generative Layout Planning from Partial Input
Zhang, H., Savov, A., & Dillenburger, B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00856 · Cited by 15 · see citing works
Self-citation
- Authors
- Chang, H., Zhang, H., Jiang, L., Liu, C., & Freeman, W. T.
- Affiliations
- Google (United States) (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 11305-11315
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313021454
- Semantic Scholar
- 7c597874535c1537d7ddff3b3723015b4dc79d30
- Cited by
- 446
- References
- 52
- Citations by year
- 2026: 112 · 2025: 132 · 2024: 121 · 2023: 72 · 2022: 9
- FWCI
- 30.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image generation, non-autoregressive decoding, image inpainting, image extrapolation, image editing, token masking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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