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Paper Citation Record · LEDGER

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth

As of 10 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 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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=sha256:86a5a19d4b07b10a41de321eed540133624479bb1cb005cfc6531652cf4ba3f6

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:a4c9ef79b8f018b0f1136ac8ea55850c6949b1ecac43ddcaf21066b1aa1b8cf1

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=sha256:68e1f4d54eb89c80949e67a296b628fcec6b6c3a528a153880ba69f578182243

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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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=sha256:09ff0d8f505f6871718f10c64baf038fc40e25d3c1f810189ea030d5f25483d2

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=sha256:859db862e0018fdb559416c5d0b99b3a054e8c2809092d73ec50b14f8bb6e3fb

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=sha256:6910aef45f9dd98bf678afea924d452c672c770fbb8300685964fe9db2e3f475

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:ed4d80828de49181b6a1fbc9aff5e4937c1151e2f20d838926a6b934b992e77c

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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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=sha256:8c9ce0ab503d21f9feebaba54c69801c015ae03de3a33069e4e178e6e9c17d79

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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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:db2a2c02d11d71acd2f914c246c4cf368998edae8694cb0bbe86cd4920823b5a

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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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=sha256:792017e996bf8a71a770712e6c22813c74bf35b1f216e08f9fe1981205b4640e

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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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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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:e308e650b8b8acf345466f507ef8eb2d6635a20e2a665809921243e6e37170f9

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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source=pdf_text observed=2026-08-02T03:08:34.059511Z digest=sha256:f10a152ec44494965d5e78eeef5a0459a9c7e5bbdb19e23cb31f732e1abebd2c

Pith citing papers

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