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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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=sha256:2ba75d385c6824ebf42aaba0b413393d24dc6d5e3ac2359ebb697f088dae9d45

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=sha256:4ff4eb7b5cafcfed25c31e5e74c6aebc61fa0e5c0c3fe2f50bc183f75e8e147e

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:d1d24376f2fb1574e0b12f29f2e63165ee486fa0300b3644750f773025808fc9

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:da14b9e9933f021893f9b2242712c9c0056f638f18f5a05968c35ecb5df48870

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:dd24c3fc6be40be687528b4596577228a2f61ad845a318ed725282a933c80fdb

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:ead5cb4d9c2ea5b07705589f50dfa45b961eb8f2fcb13498211c9c3b0a572ca7

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=sha256:2bff126fabdac12e4f81110e6301db7c15d516543b8160cd79b7e874bb075e52

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=sha256:1bc1dc6c635970d2c322cbc97ec27593d1b239789f08831d238cc07f457ff5fb

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=sha256:2cc7835f1ac3ca76f2e07e4650c058301ebcc18e89457aebbd7cee42abb76c32

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=sha256:440baf1a53db3e8e5ed6a03b3f7f6f1012a8e59da64f0e25d5215a190c608d49

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=sha256:7eb1c781038d62534cc42a48b2a7552ef45383d88eefd2b8254c0bf05cebc234

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:b5683b49c5e15544b37c1460c6a40c9737bc0ab5e7d14abde686ba0d9a70696c

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:c28183bac1dfeda0e81a221cb2f65b1e5223bc3a32af582f6534c8a70674ffaa

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:a225a8f99563e91340658cf1247beb5f1ad1804377621558844d3233a44802c8

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=sha256:893ea81ea3e48e82a85c49372f399a5bd5c84ac0d1ab43a06d8db77b14f1e407

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

This paper cites an unresolved cited work.

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=sha256:6bded807812120beb7da7fe248c0427b0dd11edb6644dad9eb73f121ec031f50

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=sha256:814faf5ffd17299a709bd881b90ef20775c8f3f27d556b4ffe847af035089376

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=sha256:1047c4486867409f8212e5f1e4630d39b844a8b0e6dc3818c0867dc586941bc3

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=sha256:0d9436e4ecdbc88ce6da07dd0f1b9c670b66a4782cb6b36e5188e9a0d7247838

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:a3d851fbaff68ee00097e6d6b81b5b650256507c52290ebf70dec42b8bd2615f

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:a38c6445415a0c3875644fee13d2ab648b60e6ca217ce63dc0bb64e2832ef77a

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=sha256:23423a300f37640c762a8985d3700a9cd57f193a30e1ac553a2541cd7c2ab931

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=sha256:06690b8d284650b45114d66acc4e4cf3bb8dc1d23c0788b7e4b4bc094213b40e

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=sha256:8a03a37611e3637a06929a6d61a5143516f50db3072263f7af25f31a1ea5721c

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:b2e2134316ef884d697c7523c190eeaca8c358d88f976df358d2d4fb38f82f23

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=sha256:6983bf3781b6e703da772b5263a4936552336159be38e0ff7a27274fe7293b2d

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=sha256:8a7f4b4717cf90d6eae900d75dde6dcec2c124e2f1585db200c2e698685f4bf3

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=sha256:1fa415af76aa85300d41d6e4f54e0f9573dcb9282cb1366ecbffcf8184f4e534

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:ab182e510f7939581e27e4cf3a4beaf2f7172e2db7aeb593e3fef0658c674606

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:dfc1e32c4894f8087f0ca7bae1c2b38961e4bf3750c7897c748c880845510cd0

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:fdd335b37e9bd5873607aef1dc23ce9f3336c688c6d86781405c78c62d146e6e

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:bf0157ac131ed2445dafb27c739b62c8b9c7868515afc72ecf05178ad2d7b2a3

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:b9cf52ddf16a2a8c75aa2991347af9acb85a926d0c69832c87209032b8a2ebd5

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:ae60b3cd4bb5be39657fc02568bc1a4a2489f936fc10d9d2625ac63ce7941d6e

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:e1785fb5cf9f64f747db755dd7ac4c994ed2f51073af30d70be18f192ac03a24

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=sha256:16860eb8274ef5aa78f352aa057c14be181aa001b197a9e7418d4f83671268c4

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:bd32458247c9531acf25ef3aaab8be537c96478cba7a40e0c774b9e0d1a81921

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:cec8ffe55e878fc409516ed41e44b5d6aa3e4e3b15f2b15e62bb090e3895c315

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:f856705eef77c9922605223e082dd9c00efae74bcd00bb8d020e2cfc9828c162

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:dd7e17e36885565ec49eff1a856e57a995f514eaf766e109f5f75ebc5989ebd6

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

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source=arxiv_source observed=2026-08-14T04:21:38.313229Z digest=sha256:75c0c7044c1b3e73ae65e76474b721df9964b320db19c54fdbf446253cad8ea2

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

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source=arxiv_source observed=2026-08-14T04:21:38.318339Z digest=sha256:83d6dfb2d534ba3dd14cfcbb2a63d189a762f3cdf1527fc66fdb53f2cf1c8d4a

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=sha256:82fbb23ec9146d4af27a2ffdd5fbbd0b605c5201d634c09afeeaa657f060afc8

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=sha256:1cfbb901fca41ae33552ebe4a1f8aa283a2d19f0a61e7d26c2a7f94d69899db1

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=sha256:733a7a2438eafdd94681e78e9d6767924ac07208506b0dff15f528e4ed9d45ab

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=sha256:4cf50a078588ef7360da08d4b75abaa7d9299bed98b7f80a9044690f77f39343

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=sha256:62f591288f22f505da77e591e4d695c75813716cc65108600074fb68ad27a367

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=sha256:1c7468033935090e4e6826cc1019899c904626e6965c4e7b406bb119cbde0f49

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=sha256:057450d81460a5cffdfbe14163793265c678d1c0c907287fc1b4036d92e67de8

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:faf1aa9db0fb53114d591710eff880929a4c7339fc84be5c14f370def027bbae

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:a2b0637e59f52d2e16ebc7458af1cc88a94137459a53841636afc883c85139e4

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=sha256:5c9fa26e37a141ddeff53dd4996fc0a9d81c51004445caa732b8edaafcddc197

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=sha256:7aae5bde97e8aab022bc2746714d2f60822361deb32cf24dc3a20bc575c5b302

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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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=sha256:8a714ab34e649cb09a752c6f5ec7ff16b5d32cba6a1875fa33d86083044822ca

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:e635b03a5445f4b15db53321ba9e6257bd586bc537215df713b25742c1b3e1a2

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:d8c57cabaeb6a7418bf5769c5541d2d0fb89e9b763dbba7c981089ed8dd7edf5

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:e6167a570c646a4c5dfa16fda40020fb2d1ba004fa480a3de7459294940e11f6

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:c039909b29f78671e1eb79085752f577d7c2d2172a2737cfef0c33d12a28838e

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:d5f711915f96280f85b9a480ad16f6fc3619a57926e4977aca9f26832600410c

Pith citing papers

No inbound Pith citation observations are available.