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Paper Citation Record · LEDGER

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure

As of 15 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2608.09417.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.09417 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:38.384143Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

59 of 59 outbound references displayed

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  • verified fuzzy0
  • unresolved59
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f74c7663-d9af-4219-b82e-ffca57c4291e · outbound

This paper cites International Conference on Learning Representations , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure International Conference on Learning Representations , volume=

Reference 1

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source=arxiv_source observed=2026-08-14T04:21:38.123721Z digest=sha256:198b11fcfa7addf1606b963e97d25e02caaf307c134c5f0cf4859a552def2c17

Observation 7cf105bc-b22d-40a6-98a4-28182fd0b1ff · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 2

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source=arxiv_source observed=2026-08-14T04:21:38.128188Z digest=sha256:05790e20377629825810b1c9f1a089a5027699bb2fba207b2f827e6ff095e0d8

Observation 7e3aa621-96d6-473b-80da-b0a404dfd4f4 · outbound

This paper cites Deep Information Propagation.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Deep Information Propagation

Reference 3

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source=arxiv_source observed=2026-08-14T04:21:38.132515Z digest=sha256:ad794154893573ba43134a74d35bb215f3cbe106b3ef091a50da28405ab43fca

Observation 0da489fd-7b24-4423-b563-15f2df7f7421 · outbound

This paper cites Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages=

Reference 4

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source=arxiv_source observed=2026-08-14T04:21:38.136887Z digest=sha256:a6995a7f55f965ab6cd5c53e27849e53939f2832c5e26697ebc2e1cedd9b8a0c

Observation 880d16f8-5fb1-45a5-914c-f3908fe1de27 · outbound

This paper cites Proceedings of the IEEE International Conference on Computer Vision , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the IEEE International Conference on Computer Vision , pages=

Reference 5

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source=arxiv_source observed=2026-08-14T04:21:38.142143Z digest=sha256:b8c05f251f4bb97adee4bfabad8a1c71f7ec86d7727595811a32fe00e03a2277

Observation c62f5cd3-f4d5-4f5b-a9e2-23f4fe37258e · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 6

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source=arxiv_source observed=2026-08-14T04:21:38.146460Z digest=sha256:7db3e17ac7aaa4e7eb9f07208688a650a20cf43944bcbb87f6748f03fe02a1ea

Observation e3df0a2b-ffc5-4735-ba97-8b626082d927 · outbound

This paper cites 2012 , publisher=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure 2012 , publisher=

Reference 7

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source=arxiv_source observed=2026-08-14T04:21:38.152125Z digest=sha256:bbd6b021a2df92a7372306d493e8053610ea4e401584b36744fcd4e835891da6

Observation 00e2637e-ff86-4104-9879-2c639648f955 · outbound

This paper cites Deep Neural Networks as Gaussian Processes.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Deep Neural Networks as Gaussian Processes

Reference 8

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source=arxiv_source observed=2026-08-14T04:21:38.156188Z digest=sha256:d3c4c1b679b0315c3e3565814d7c8c41ed389946d15fdfcfc1efe3a740abe6d6

Observation 43ed4e52-4570-44f5-b875-b0109803ec84 · outbound

This paper cites Gaussian Process Behaviour in Wide Deep Neural Networks.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Gaussian Process Behaviour in Wide Deep Neural Networks

Reference 9

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source=arxiv_source observed=2026-08-14T04:21:38.162038Z digest=sha256:2261f84f84c0bb165d59a661506ccb427c1a6f58f7465d9098e8278f18266b3a

Observation 7f97f1bc-b81f-42d1-9ff7-19a17fec67c7 · outbound

This paper cites Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice

Reference 10

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source=arxiv_source observed=2026-08-14T04:21:38.166091Z digest=sha256:9a775895866a7be74be9aeb69bfb77b0294b98f885b32e755b0f85a85bac6558

Observation 9cf98165-b1d8-4980-8d31-20ac87d81c85 · outbound

This paper cites Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Reference 11

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source=arxiv_source observed=2026-08-14T04:21:38.175506Z digest=sha256:a92b927265994283ebe77ed0ffe2787320cca2d1144bf20f8bd68295013b01ef

Observation baf7eeaa-a249-4159-9d78-7674ace95e64 · outbound

This paper cites Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks

Reference 12

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source=arxiv_source observed=2026-08-14T04:21:38.180759Z digest=sha256:3ccb476eaca1e077cd8b3006876b8fac85b03ed54aaa3afd3b2a5f7141652c62

Observation efb6ea85-815d-4c7e-84dc-c30f4167a005 · outbound

This paper cites Classic.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Classic

Reference 13

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source=arxiv_source observed=2026-08-14T04:21:38.185670Z digest=sha256:ae99fbfee4c750579a2e6219efbc387b2bcf1c9e09ce98938049d6b428f2beca

Observation ff81a120-2346-46e4-863f-b76336d4faf2 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 14

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source=arxiv_source observed=2026-08-14T04:21:38.189519Z digest=sha256:9436622c4f367d08836d301a95ad9505b7f2c37fb5499f353b7c76deb00567a4

Observation b6983274-58b5-4a36-ad5b-13f77ef0be6d · outbound

This paper cites International Conference on Machine Learning , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure International Conference on Machine Learning , pages=

Reference 15

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source=arxiv_source observed=2026-08-14T04:21:38.199014Z digest=sha256:93886c852db4b33a6f016cabe8e695301cd72e572ebc300f8b9c0e17d53b8956

Observation fd79adb8-91a1-45ed-8026-1a253fd3dc7e · outbound

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Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Unresolved cited work

Reference 16

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source=arxiv_source observed=2026-08-14T04:21:38.203406Z digest=sha256:9c6a84d4fab3b32c6cc2d0ae1fba01e7b8b94ca23f31b352afb941a4970bbf49

Observation 74f895fb-5e99-46ad-8e2d-3a32928ee970 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 17

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source=arxiv_source observed=2026-08-14T04:21:38.207831Z digest=sha256:7248e868a5ead89d95f86271c197af881cca02cd29861671f959f16353c406ba

Observation d8177392-84a3-4afb-9dad-91586138b85c · outbound

This paper cites Layer Normalization.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Layer Normalization

Reference 18

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source=arxiv_source observed=2026-08-14T04:21:38.213799Z digest=sha256:68f8496d43211aa21871496670c18ef583a24d1f12b1249ddf1f1bd962d89377

Observation 1cb01525-5f2c-4ec4-8016-c2575633e9ce · outbound

This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

Reference 19

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source=arxiv_source observed=2026-08-14T04:21:38.217808Z digest=sha256:157901f11fcdd182bdcb086a845366d79946a4dc22f05fb37dea8095522dd640

Observation e27a27d6-512f-446c-8d5d-99103f9a673c · outbound

This paper cites Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers

Reference 20

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source=arxiv_source observed=2026-08-14T04:21:38.221472Z digest=sha256:9fae6ea48054ca1327e990046715893405abcf37065e089033d7a8443bd05b6a

Observation fb86b79b-9440-4cb5-bbf7-041f31d48da6 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 21

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source=arxiv_source observed=2026-08-14T04:21:38.225325Z digest=sha256:cae7ba3f490fce1f91b0d503ead69118a6c67165874178ef029cddb964ee69cb

Observation c18be136-d616-445c-a062-9ca3be07b15b · outbound

This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=

Reference 22

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source=arxiv_source observed=2026-08-14T04:21:38.229044Z digest=sha256:8b2827b874c50509354c93802531d90b53acd289244a493b79a2e0a27def72c7

Observation 739af441-0bf2-4bc9-9c1e-b13ac165e496 · outbound

This paper cites International Conference on Machine Learning , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure International Conference on Machine Learning , pages=

Reference 23

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source=arxiv_source observed=2026-08-14T04:21:38.233082Z digest=sha256:1aae34150f8e684e70e01db100bf52d930415500df4a60e331994f3f70392d48

Observation ea6218c9-5ada-40a0-8355-d728d2e44927 · outbound

This paper cites Transformers without Tears: Improving the Normalization of Self-Attention.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Transformers without Tears: Improving the Normalization of Self-Attention

Reference 24

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source=arxiv_source observed=2026-08-14T04:21:38.237430Z digest=sha256:2887a5319a826c368540b1ae24e224f55b12b0f7795de6b4ee3f4d6aed927f6d

Observation cb7dc440-1c1d-4873-bab7-e21185a317a3 · outbound

This paper cites Uncertainty in Artificial Intelligence , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Uncertainty in Artificial Intelligence , pages=

Reference 25

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source=arxiv_source observed=2026-08-14T04:21:38.241614Z digest=sha256:ad174f096af3fb12f6aeb68c6f4ab4ed7bba354406f3f090d7c1013aa7b43e13

Observation 94578d9f-cf14-44e4-8e6c-d39119724c9a · outbound

This paper cites Query-Key Normalization for Transformers.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Query-Key Normalization for Transformers

Reference 26

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source=arxiv_source observed=2026-08-14T04:21:38.245284Z digest=sha256:2c4d6d737f0ea015c47c7993f828732672d0b5689ead21c4e733f7e9c3fcf1d4

Observation e2e18924-66f5-49bb-a116-620e0f7528d9 · outbound

This paper cites International Conference on Learning Representations , year=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure International Conference on Learning Representations , year=

Reference 27

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source=arxiv_source observed=2026-08-14T04:21:38.249111Z digest=sha256:037708d672959fdc96f840cb46e3c7c74a8f908aef2f19e2165aceb93ab0efe2

Observation 6ffc9ff4-5a46-43d5-b4cc-32bf7a2d6d25 · outbound

This paper cites International Conference on Machine Learning , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure International Conference on Machine Learning , pages=

Reference 28

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source=arxiv_source observed=2026-08-14T04:21:38.253407Z digest=sha256:37a2e72fc00fa5f0d475bce12e4fe6095cdff1263d74d43dd0dde3ea01a7d62e

Observation 522fa13b-147d-4acc-ac69-9cc76be05437 · outbound

This paper cites Representation Degeneration Problem in Training Natural Language Generation Models.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Representation Degeneration Problem in Training Natural Language Generation Models

Reference 29

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source=arxiv_source observed=2026-08-14T04:21:38.258563Z digest=sha256:21dd9483ec8d075256bbcb401312010e5591cb7cd6a5730e94d587ff1951af38

Observation 1125f841-9d4e-4564-be6e-bbcd90ec4df0 · outbound

This paper cites Fixup Initialization: Residual Learning Without Normalization.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Fixup Initialization: Residual Learning Without Normalization

Reference 30

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source=arxiv_source observed=2026-08-14T04:21:38.263899Z digest=sha256:02d661bc2954a8374491376928dfa47224c1a9d136fc720fd183261236fc057e

Observation 390dcd6b-1c9a-4736-96bd-fe3dcb54af40 · outbound

This paper cites GLU Variants Improve Transformer.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure GLU Variants Improve Transformer

Reference 31

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source=arxiv_source observed=2026-08-14T04:21:38.268216Z digest=sha256:8735c04fd5f8a4ae4e48d6a9d066c5fffb19fb80937464521367fd6fb3989f32

Observation daf62005-8b19-4569-b172-4a161ccdf761 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

Reference 32

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source=arxiv_source observed=2026-08-14T04:21:38.272607Z digest=sha256:261497bac0543e5111837c0e63b24f9e77ff56b2657b470001fbea632be676ad

Observation 62ca9c71-053c-477e-a4fb-e0a4adb6d44a · outbound

This paper cites RealFormer: Transformer Likes Residual Attention.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure RealFormer: Transformer Likes Residual Attention

Reference 33

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source=arxiv_source observed=2026-08-14T04:21:38.276925Z digest=sha256:784175bbe00496e1f2a57553110035d29b802fe8434f9f2c9e9d7576cff80055

Observation a483963a-9ce4-49ec-8ff6-d76413dd6f0a · outbound

This paper cites Do Transformer Modifications Transfer Across Implementations and Applications?.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Do Transformer Modifications Transfer Across Implementations and Applications?

Reference 34

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source=arxiv_source observed=2026-08-14T04:21:38.280691Z digest=sha256:11f84b92476eb8cc1e651359eb89bd1a5819ddd5860145bd39920d78563c9eea

Observation 7a9cb568-1a6c-4a11-92d1-15cde46bcef3 · outbound

This paper cites NormFormer: Improved Transformer Pretraining with Extra Normalization.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure NormFormer: Improved Transformer Pretraining with Extra Normalization

Reference 35

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source=arxiv_source observed=2026-08-14T04:21:38.284545Z digest=sha256:0e5d6210795e050a714e533b7127c6d97e38be7c49b991927d688f66cc41615f

Observation a1d4ee16-ab05-41fa-9f3f-91b82e7dba05 · outbound

This paper cites Revisiting Over-smoothing in BERT from the Perspective of Graph.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Revisiting Over-smoothing in BERT from the Perspective of Graph

Reference 36

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source=arxiv_source observed=2026-08-14T04:21:38.288594Z digest=sha256:ff85a7abbcc64d2da5c249822d92e3e6688e522fdd00da30464ebab2d0895a75

Observation 01294cd2-6324-4890-a278-9c8ef2cb9ef0 · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 37

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source=arxiv_source observed=2026-08-14T04:21:38.292787Z digest=sha256:1563b594974a5d05393dc993bcb9189dac7ac53acae4f601d11a0ab5eecd0cb4

Observation 986fa79f-6904-4ca3-b909-916fa2a1a388 · outbound

This paper cites Uncertainty in Artificial Intelligence , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Uncertainty in Artificial Intelligence , pages=

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.299347Z digest=sha256:19469044e2065fabbbee9368694cabf663cd73aaea3691774e28567ce819702b

Observation ae81cdfd-df89-4bae-9447-37c0aef5ee5a · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 39

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no resolver link, observed 2026-08-14T04:21:38.303040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.303040Z digest=sha256:d46cb2d0dde251147e61ad84d5e5fd1642af94f14aa2cf2c932378e8e48fe501

Observation dd3bb6c7-822a-424f-8bc9-182e79bf21ff · outbound

This paper cites Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

Reference 40

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no resolver link, observed 2026-08-14T04:21:38.306868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.306868Z digest=sha256:40186c7f667dd736919aa5ff20aad6c88771ced235c88485a2c7354aefc98ed9

Observation 3793dee4-538f-4888-84b6-17b21f9701bb · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 41

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no resolver link, observed 2026-08-14T04:21:38.313229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.313229Z digest=sha256:ba4a2d59063df9172417d7e178d691e4a4ba9d60344d9530cbedd2f0033eb151

Observation fb0cf219-3f3b-48e5-b18d-1be389b8a34e · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 42

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no resolver link, observed 2026-08-14T04:21:38.318339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.318339Z digest=sha256:9f68eaa8167f9660da3930cb46cbff42b2f5e3ae2d640532924304aad7863be4

Observation 2817d25d-60d3-4363-a82d-0e38e02769b5 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2023 , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Findings of the Association for Computational Linguistics: ACL 2023 , pages=

Reference 43

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no resolver link, observed 2026-08-14T04:21:38.322236Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-14T04:21:38.322236Z digest=sha256:df7525b02d26ebafde64c748815d98c6aaecb6faf4934e26eb889cb19288e3b7

Observation 1a988e5c-76d2-4c1b-992c-8c5ac7ff6b4a · outbound

This paper cites Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation

Reference 44

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no resolver link, observed 2026-08-14T04:21:38.325853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.325853Z digest=sha256:c0bba11ccbb78721020c3f0c3bbba30f14f567a82c363ad498a998f4de4d53bf

Observation cadfe642-be91-4cd2-a17d-721b9960c43d · outbound

This paper cites ResiDual: Transformer with Dual Residual Connections.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure ResiDual: Transformer with Dual Residual Connections

Reference 45

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unresolved
no resolver link, observed 2026-08-14T04:21:38.329641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.329641Z digest=sha256:969117ca979cbf7110882628f8b6ef344e28066b5f2a60aba83ad9611d9c5dd2

Observation 3a1f7dec-477c-4b2b-a13b-3fa27dd36a2c · outbound

This paper cites Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models

Reference 46

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no resolver link, observed 2026-08-14T04:21:38.333325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.333325Z digest=sha256:fbc5c95ce5b7c1a35f767a6530b5ac5e3c44196f0c58df7322a53c693f0a44ab

Observation e149d968-3275-4d99-b95a-2a163cc82bf5 · outbound

This paper cites Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

Reference 47

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no resolver link, observed 2026-08-14T04:21:38.337114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.337114Z digest=sha256:91624f89961834110cb84218194c33c73f0972447894f7c1385518d355ea9c52

Observation 40fef9a5-0b9f-44a6-921b-caa555f5774d · outbound

This paper cites an unresolved cited work.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Unresolved cited work

Reference 48

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no resolver link, observed 2026-08-14T04:21:38.340692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.340692Z digest=sha256:cb143813c1b3220738f8a7c792cd313ecb567857225e1fe3c0e7b494b46491db

Observation 10e4fcea-a7b6-4051-aa37-36dfa972e16f · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 49

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no resolver link, observed 2026-08-14T04:21:38.344912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.344912Z digest=sha256:e1790458e0756f6d054171fdd888f38a3e186de2548a015e8147f0750de4fc3c

Observation 521ef5a7-36bd-426d-84d5-68e0603eda13 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 50

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no resolver link, observed 2026-08-14T04:21:38.348675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.348675Z digest=sha256:2d8c043d712ef0c8cfc02ab00dfccca17c02d4ff12d597c115a6017ba582b68f

Observation 765c2a44-7301-4137-9aa2-2a752c16cf76 · outbound

This paper cites Scaling Laws for Neural Language Models.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Scaling Laws for Neural Language Models

Reference 51

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no resolver link, observed 2026-08-14T04:21:38.352066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.352066Z digest=sha256:49c56d168f1f30bc63d6b2f90734dcfca223a4febe68486a4fc66f27bda45a0e

Observation 6cc02f1f-0fa4-4cef-865c-a218e0108a8e · outbound

This paper cites Training Compute-Optimal Large Language Models.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Training Compute-Optimal Large Language Models

Reference 52

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no resolver link, observed 2026-08-14T04:21:38.356001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.356001Z digest=sha256:65e73bcf04a634c46819577d89fa10c828468cf175c9d3d88d09db7223e4c447

Observation 5b853770-ce91-4188-a3e7-7583fe7fb5b1 · outbound

This paper cites Decoupled Weight Decay Regularization.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Decoupled Weight Decay Regularization

Reference 53

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no resolver link, observed 2026-08-14T04:21:38.359922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.359922Z digest=sha256:a1beec0f6abac37304b5bb0cb1deeb5985e49d460869ce68f7b03bada7336f13

Observation e9ebae4b-41d5-40b8-85a1-41fc4afc03e9 · outbound

This paper cites Journal of Machine Learning Research , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Journal of Machine Learning Research , volume=

Reference 54

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unresolved
no resolver link, observed 2026-08-14T04:21:38.363798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.363798Z digest=sha256:368c25d72e40a6853cead61a456c55f26c9c5d21750053d1ac6401d354848789

Observation 4773ea73-9edb-4821-885c-4d2d8cb16f80 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 55

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no resolver link, observed 2026-08-14T04:21:38.368091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.368091Z digest=sha256:ded0bce62ead06acafb1f7422ce120f36cf5853723d0c9d5470167498c72a096

Observation 6df78ccc-6302-48db-be7f-8667641b8f8c · outbound

This paper cites arXiv preprint arXiv:2602.18849 , year=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure arXiv preprint arXiv:2602.18849 , year=

Reference 56

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no resolver link, observed 2026-08-14T04:21:38.372095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.372095Z digest=sha256:40e6e40bcdc2e224a21bb4d4d3432f44471f1d0bdc2d3d57ead36bf7921ca08b

Observation eb0eb5e2-2bb4-4697-aa7d-971d99f474d1 · outbound

This paper cites arXiv preprint arXiv:2601.19895 , year=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure arXiv preprint arXiv:2601.19895 , year=

Reference 57

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no resolver link, observed 2026-08-14T04:21:38.376379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.376379Z digest=sha256:0f3c9ffd5370d76a19ad4948c93cfc148842c741ade9e289bf73eb792951105d

Observation e3dd11c8-1d48-4f6a-b40f-0590ffbdb8ca · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Advances in Neural Information Processing Systems , volume=

Reference 58

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unresolved
no resolver link, observed 2026-08-14T04:21:38.380478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.380478Z digest=sha256:7614a87f3c657c4122d2697c84c63581ecce51fa5533234fdb17ff027c1c273e

Observation 5423d62f-a521-44c8-a78d-c217b38f5494 · outbound

This paper cites an unresolved cited work.

Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure Unresolved cited work

Reference 59

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no resolver link, observed 2026-08-14T04:21:38.384143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.384143Z digest=sha256:bfbc869b5e48c051956aac47701bf77a3e99852369edc96915d82d0fd3558510

Pith citing papers

No inbound Pith citation observations are available.