Papers › General-purpose, long-context autoregressive modeling with Perceiver AR

General-purpose, long-context autoregressive modeling with Perceiver AR

15 Feb 2022arXiv:2202.07765archive 2025-07-28

Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse Engel

Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed to capture this long-range structure. We develop Perceiver AR, an autoregressive, modality-agnostic architecture which uses cross-attention to map long-range inputs to a small number of latents while also maintaining end-to-end causal masking. Perceiver AR can directly attend to over a hundred thousand tokens, enabling practical long-context density estimation without the need for hand-crafted sparsity patterns or memory mechanisms. When trained on images or music, Perceiver AR generates outputs with clear long-term coherence and structure. Our architecture also obtains state-of-the-art likelihood on long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books.

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google-research/perceiver-ar officialmentioned in papermentioned on GitHubjax report
krasserm/perceiver-io mentioned on GitHubpytorchApache-2.0 report
lucidrains/perceiver-ar-pytorch mentioned on GitHubpytorch report

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4ran · our draft was wrong
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CausalAttention lucidrains/perceiver-ar-pytorch/perceiver_ar_pytorch/perceiver_ar_pytorch.py community (archive-listed) ran · metamorphic tier: invariant MIT (permissive) · a265de65b3c70e54 · report
FeedForward lucidrains/perceiver-ar-pytorch/perceiver_ar_pytorch/perceiver_ar_pytorch.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 7e6f6a18c63a8be3 · report
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apply_rotary_pos_emb lucidrains/perceiver-ar-pytorch/perceiver_ar_pytorch/perceiver_ar_pytorch.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 746466b4822c61fb · report
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PerceiverAR lucidrains/perceiver-ar-pytorch/perceiver_ar_pytorch/perceiver_ar_pytorch.py community (archive-listed) unverified MIT (permissive) · d0f7f44834342f9f · report
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is_checkpoint krasserm/perceiver-io/perceiver/model/core/lightning.py community (archive-listed) unverified Apache-2.0 (permissive) · 9ac70921db9fdb73 · report
positions krasserm/perceiver-io/perceiver/model/core/position.py community (archive-listed) unverified Apache-2.0 (permissive) · 6534b6aca89ab357 · report
get_sweep identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 3ce096de610c4729 · report

Tasks

Density EstimationLanguage Modelling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling WikiText-103 Perceiver AR 358M Test perplexity 18.4 #36 of 89 Archive leaderboard report

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