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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
  • metadata mismatch0

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:aa050dace54a0dbc39dcc35f3e1a473c18b7020f32a9e25063e4c01fd208aa07

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=sha256:88c12bc788c98d6021ceb847d22e2e6f48d837a777143137657aa318ec520ee4

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=sha256:39311de395b700ee23a54758fd543236c36424867df2770e4c338153f8da1dca

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=sha256:3a71d3b9ec4a5c6e35708fda39946eb8ee4dd7e87f70d616061bc215c0eb8b0a

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:de12d157fc9ac48b382c5c05798e9608fe449494e7e7bf62642e2a6295e81602

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:c1d2f8e9d4460af103cd4cdf29c09cd965e107eb0d2baac989bb9398863590d0

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=sha256:4ced566071e2dfab0d73d60468ec14d2714d37f927a244571a7e9888b90283f5

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:a3aa93e9c3edf5c0227b3158ddc28a6c7e1a11e0799a4f8ed41a4bb6f8bda72b

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=sha256:39fa90f51b4a9744a0580477d5c3c1a6534bd9402bce7f128ab5d2bb149fab0d

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=sha256:4abcc74a06cad0172659a43d3bec9a823ca1a251c209ab869e7786db7e2093af

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:d22397325720fa80277a9108835adbaa568cf936a6b1ae4caef3771e6cd84839

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:bd22f561364bfd502de5c61ce09525b3d7912f08e6a6c4d9436f0b1b0b013228

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=sha256:38f814e4525883177b3eefd5841bda19b4752b895986a705c5d8c01ff2778800

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=sha256:0dccae9e39a15506018d4ef0e2d7ecaacbe606eedae90896bd7aaddee8ee9c7b

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:b78ab5f20d1840eacc635f793faeccb424a8c455a55a6fac6d09ecf17e98e460

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=sha256:008981e9df3d4627aa2851211c1d2ab46e32e1ec1b3f9865d5d438221c2c4b18

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:b0fc80923646f6c6d9cf249d5d97c5e8ba87d9ffb847a3f3f1a81d48338804cc

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:d84966cadc11341e2da8872f6575718e9e5ebb0c10473e550de651ddb504d5a6

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=sha256:6d7582d271ea82ad1fba0a958660f44e2afc73c7c77fe976a82c790d6f1b945f

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:e9102518f649222d70ef70ecfe532377943321a2943a30cf228cd5430e0ce593

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=sha256:27f76dc9a58b58d9c2db7a096b6387ab57e1034b5dfdfa2b49a1d94addac08b9

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=sha256:96a38c5929ce9a31b8f3c0d23fd2d6a58b73546cd0092d645d35457ea6b7916d

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:e325b270c071a3ca226daf5713639f4911f36fb4e29c18b19cc2394250e76e43

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=sha256:1cd768d63f7420bc261f8664d79c7fee9d299544146b4a2123ef2ff0cf5ffa82

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:d9566a81cfe71c0af2240debd552bacd81545b23f7581c80b57fdb0d2434b709

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:bb4b450cbe58466933846a2860419c1bd6fd11af9cd3d6a30bebac75d6af1fe9

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=sha256:529a68c0b7696c7b53d330d9bcc2ffb7fec6afa2e7d6921b2b9f33a7de6ae334

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:e0c4f13546dcc99a40435ba6fef9a64969c7eda7c00850a1f99a073ac2045106

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=sha256:41077d7e60bdf2adbcc6f6ffb3e1396ea60216a6733b6eed1bae6f9178a794c9

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=sha256:129f87440f0c686323aa01ae9fc04a44cbb625cdc7f9aedc0df031e47800b6a8

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:e4862a4dcbc52be3e1fd5a607176be3693e515b03f47a93327722ec71df066e3

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=sha256:0900c083b7103b9e7b507f35c2d1799d226ca1cf9e2b9ce386c7bf0ce83380a1

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=sha256:079131d5b6bd83da1ea3448b1ece97743be0eaa8dab8eba2289cdfab01922513

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=sha256:9d7f00da0baa917e78bbb4e47431bfdf5ff6979f569c8a23626349b2eef84942

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=sha256:522bee2df2c0e242a84efd0d072298dd4702d54fb9c6330d9867e3a1ec2b3cb4

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=sha256:52ac8d6d0bf445ea569c3db2044d9cc92021bf12ad10592f9f9e46dceb9a0f80

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=sha256:8a2a6af7c1ef049fa304aea1f97a03ddbcb949c67b1517c5c6eb47f63ee6f811

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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no resolver link, observed 2026-08-14T04:21:38.299347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.299347Z digest=sha256:91b048cfd8655d7d9852dbf3ae194192147665fbe540cbdb66d49d74fa8caa16

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:c0a3b86d4e42edcbeabc9119eaf4c9ffabb6aa69f6f9b68d766ee687193cc093

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=sha256:5d38924ed652c9a1319fa999785033c820914e0c99a9bc0f4eceff7524303c57

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=sha256:881337ca709ae75e4a570f0fe072bc04788c7c59f4febe9988b05b9dd86aaa89

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:fd454fe59fc378067b23120691c4fea38ffbff2212dd44a71f16a391c11b7d9c

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:21:38.322236Z digest=sha256:b311cdea9144b3df78d2f7795551a70bf3ab7f894ca51cfc0348810e6cc49a1d

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=sha256:7b6ef6f256899ffff435bd116f3ae303703bf98a5023cbd4cd3996f103a83fb3

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=sha256:5ee11f522d3639aa228c6b617120b8f31bab12aadc49d44968ca67cced22e188

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=sha256:3ca7068dbe5470239ce555b6912b7917cff24a13d84b61ed6861b0e6ebdc0133

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=sha256:76ff4255127c0b3b3f668a9c1bc0e47e691e82fcf40e65971b43b3a35e12240f

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=sha256:5fc5dfc9e64da499d6055844cb6b1d3d6689619896957b134264890f33bb664f

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=sha256:6fd70e990b8878260dabd74a4dc61c305e814d088875e433a2dc6d82c0a6f456

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:eec9a9440c7059f1882bff8d9fb09f695ccc387e4826118337ba4f236370a92f

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=sha256:0795b14633a284bd924db2184014c091ea82a0e715a6c61349a7d40b9681a216

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:e98a9334574c5f10ba853cdcac1643badfec1a7b79af2e84a654116e7e71bb68

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:ee5d3d5e80ecae4f665e3b132cf92efeb212bc5ec5b83fe068656fb7add35d75

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=sha256:3061cebec489e202d30ece2c4925361519d913cc902dec8f0c9a6921e203aadf

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=sha256:7ae1d5c1f0ceb06bea13645262fe8be0e44164d2a5603bb11b25efaf5da1f0b6

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=sha256:54272911c68a3ced729ab45000bc33d0bcd51d9082c114a79628368b3cdbe0c9

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=sha256:1bb5aaa21c9fd4c28248095a6a6eccb6fe01d63232100d41ec8272c618fa8221

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=sha256:5a8a7a29b30d898b59d3945aa95b56347c8271ea824cc27f540b1b6e2ff227ec

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:a18ac392b57c0032d21d8c041281559bd04ccbbb5bcb60a03cbd8abb2a6d62a1

Pith citing papers

No inbound Pith citation observations are available.