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Paper Citation Record · LEDGER

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.14018.

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

pith.paper-citation-record.v1
2607.14018 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:08:35.920695Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation e48fb2be-2297-48ac-8c9a-88159c794113 · outbound

This paper cites Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse

Reference 1

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source=pdf_text observed=2026-08-02T03:08:33.307962Z digest=sha256:805902d7f715ec8879578546b5692d4078627f42521a45998d5b592ada5a9791

Observation 00a60409-d957-4b5e-998b-be61f14b88c5 · outbound

This paper cites Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse

Reference 3

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source=pdf_text observed=2026-08-02T03:08:33.459795Z digest=sha256:80b445439da7c620a0b3129ba684ceb5827f1d3242168c4300ea27a5d8f0b1ef

Observation 2f78be75-bc4c-4ba5-b114-99c6adc04c26 · outbound

This paper cites Each Sk multiplies k branch Jacobians, so it inherits the rank deficiency of a k-fold branch composition, weighted byβ k.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Each Sk multiplies k branch Jacobians, so it inherits the rank deficiency of a k-fold branch composition, weighted byβ k

Reference 5

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Observation 273679db-de8d-4d2e-aed1-ea71ae1f0cad · outbound

This paper cites A dash marks a configuration that does not reach0.4at any learning rate in the sweep (3×10 −5 to10 −2).

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth A dash marks a configuration that does not reach0.4at any learning rate in the sweep (3×10 −5 to10 −2)

Reference 6

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source=pdf_text observed=2026-08-02T03:08:35.823673Z digest=sha256:3ec5dfea0556d0c49b9251eb339c20419c2f9914c75a421b05cfbe8d8f771409

Observation 06080479-a95f-48bf-8ee2-dbbaec113f33 · outbound

This paper cites Exact attention sensitivity and the geometry of transformer stability.arXiv preprint arXiv:2602.18849,.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Exact attention sensitivity and the geometry of transformer stability.arXiv preprint arXiv:2602.18849,

Reference 8

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source=pdf_text observed=2026-08-02T03:08:33.954746Z digest=sha256:fcf405cc424f9430dfd2e32574e068fee308049206866cf67c4014a0e551035f

Observation a18b744d-80b7-49d7-8bff-d8873eeca8f7 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Gemma 2: Improving Open Language Models at a Practical Size

Reference 10

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source=pdf_text observed=2026-08-02T03:08:34.141662Z digest=sha256:59ec91651235afbe1bceb09c79b757239583bf983fa9faafd2866a7e960bccdb

Observation dee6e0eb-ce83-4d74-a0f4-2e1b2064ac60 · outbound

This paper cites Gemma 3 Technical Report.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Gemma 3 Technical Report

Reference 11

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source=pdf_text observed=2026-08-02T03:08:34.224246Z digest=sha256:aac5c6b6a2387c3fb91abaa381b7a0e173fe7335f7fa3c1ca073c51dd45f30fd

Observation bff7ff00-57e9-4fef-bd98-e4b29c746f49 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Kimi K2: Open Agentic Intelligence

Reference 13

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source=pdf_text observed=2026-08-02T03:08:34.417739Z digest=sha256:1fe374f7805f0b4de3b6b854d862bd33e6724ac3106fcd005a20ce7d8eb76b6a

Observation 18987e3a-67f6-440b-b156-fb44c1a64ccb · outbound

This paper cites BranchNorm: Robustly scaling extremely deep transformers.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth BranchNorm: Robustly scaling extremely deep transformers

Reference 14

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source=pdf_text observed=2026-08-02T03:08:34.552050Z digest=sha256:07626bead7c61ded05e1c6bb8d86675f13eb1cb11e872b1a20ef6adaf8848d06

Observation a94b6d69-0fe4-42f2-b1e5-d02798593350 · outbound

This paper cites The Llama 3 Herd of Models.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth The Llama 3 Herd of Models

Reference 15

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source=pdf_text observed=2026-08-02T03:08:34.611621Z digest=sha256:27da19d3cd3312bb48f828e76e3961387744ff426a9a530d299885d17446c280

Observation 7d316a65-7278-4e75-9304-53ef6fb70121 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth gpt-oss-120b & gpt-oss-20b Model Card

Reference 17

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source=pdf_text observed=2026-08-02T03:08:34.824744Z digest=sha256:941a566242b804ff76adb0f6f0e684dc4396d2b7b4ad57d8d4eed2fbcb7c79d7

Observation 9ae25059-0d74-4d40-89f4-ef847f09db69 · outbound

This paper cites Qwen3 Technical Report.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Qwen3 Technical Report

Reference 18

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source=pdf_text observed=2026-08-02T03:08:34.907923Z digest=sha256:868082ae5d6fd6f50eaf583f451f7cb9634a14e1b682e0a23be9153b2b50cf9d

Observation 900e3749-5352-4242-bdec-812979d4370d · outbound

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

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth NormFormer: Improved Transformer Pretraining with Extra Normalization

Reference 20

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source=pdf_text observed=2026-08-02T03:08:35.091316Z digest=sha256:13ae0f556dc016a4aa4595bbd810c4ba22170284b45c7c6c7dcc206c916dc456

Observation 5889ff37-be9f-4862-aada-7d1bbd9ff7fc · outbound

This paper cites B2T connection: Serving stability and performance in deep transformers.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth B2T connection: Serving stability and performance in deep transformers

Reference 21

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Observation 827d113d-7c31-4d7e-86ac-3bb3ffb716f9 · outbound

This paper cites Tensor Programs IVb: Adaptive Optimization in the Infinite-Width Limit.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Tensor Programs IVb: Adaptive Optimization in the Infinite-Width Limit

Reference 23

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Observation 87a008c7-51b4-4bbd-9f8e-1c512d8b3f6e · outbound

This paper cites an unresolved cited work.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-02T03:08:35.741338Z digest=sha256:021a180158e4903606a4f941d93b76744479ef18aea17a4932f5a2c991f0e6b1

Observation 0e816ea9-e4c7-439a-8548-a9838ded34b0 · outbound

This paper cites (c)A centered activation has no mean to align (mean−0.000).

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth (c)A centered activation has no mean to align (mean−0.000)

Reference 27

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Observation 1dd2402c-3f46-455e-9225-5101237e875b · outbound

This paper cites We call the expected fraction of units with nonzero derivative the survival rate,p(σ) = Prz∼N(0,1) [σ′(z)̸= 0].

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth We call the expected fraction of units with nonzero derivative the survival rate,p(σ) = Prz∼N(0,1) [σ′(z)̸= 0]

Reference 28

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Observation bc5f3824-41da-44c4-b328-d09da15f831b · outbound

This paper cites GLU Variants Improve Transformer.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth GLU Variants Improve Transformer

Reference 2014

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Observation c4654426-a0e4-448e-b38b-6af51e9993c0 · outbound

This paper cites SpanNorm: Reconciling Training Stability and Performance in Deep Transformers.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth SpanNorm: Reconciling Training Stability and Performance in Deep Transformers

Reference 2016

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Observation 226ffbf1-939e-4791-b448-58554c01edd8 · outbound

This paper cites Layer Normalization.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Layer Normalization

Reference 2019

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source=pdf_text observed=2026-08-02T03:08:33.370890Z digest=sha256:a6d2a99e696b931a96ad908ea07fd805ce82884cb7835a4c92b1c1ddc43be5aa

Observation 3621f71f-ef55-46cb-9473-39f0fac4c6ab · outbound

This paper cites DeepSeek-V3 Technical Report.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth DeepSeek-V3 Technical Report

Reference 2020

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Observation 7a6800d9-0742-4de5-b3a5-7b6ece99915e · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 2021

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source=pdf_text observed=2026-08-02T03:08:34.681235Z digest=sha256:42aeef582c2efc9867c6e8fd2b9996ba1946cf7afd35e8b6a8c1a16fc957b94b

Observation d11e4e1e-c8cd-43b4-981b-c6921e56d763 · outbound

This paper cites When do spectral gradient updates help in deep learning?arXiv preprint arXiv:2512.04299,.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth When do spectral gradient updates help in deep learning?arXiv preprint arXiv:2512.04299,

Reference 2022

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source=pdf_text observed=2026-08-02T03:08:33.607706Z digest=sha256:8d2700416b8b86aa169f8e790a5fa6077cd72fd60ad44d8b0f606c0f7c4cd33d

Observation 7734077f-6a38-41c1-bf25-19947ff1f201 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Gaussian Error Linear Units (GELUs)

Reference 2023

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Observation 36a606cc-72b7-457b-b9f5-7d88010952b2 · outbound

This paper cites Post-LayerNorm is back: Stable, ExpressivE, and deep.arXiv preprint arXiv:2601.19895,.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth Post-LayerNorm is back: Stable, ExpressivE, and deep.arXiv preprint arXiv:2601.19895,

Reference 2024

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source=pdf_text observed=2026-08-02T03:08:33.527787Z digest=sha256:ebb8006db3b59441de9f178d14a67749213321714fe48f78f134aa2d9226c168

Observation 441c7ac1-0dea-4e4e-8ebd-64729a209e57 · outbound

This paper cites On residual network depth.arXiv preprint arXiv:2510.03470,.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth On residual network depth.arXiv preprint arXiv:2510.03470,

Reference 2025

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Observation e396381d-7467-4da9-bf64-adcbd04e9bc0 · outbound

This paper cites The depth delusion: Why transformers should be wider, not deeper.arXiv preprint arXiv:2601.20994,.

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth The depth delusion: Why transformers should be wider, not deeper.arXiv preprint arXiv:2601.20994,

Reference 2026

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Pith citing papers

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