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Paper Citation Record · LEDGER

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2601.22580.

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

pith.paper-citation-record.v1
2601.22580 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:37:55.054757Z

measured 26 of 26 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:08:35.290649Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 293ba4ee-e339-4786-80db-b2dbe1f379f1 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 1

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Observation 0d237581-7563-45b5-bbde-8f33e8f20485 · outbound

This paper cites an unresolved cited work.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-03T06:37:52.174764Z digest=sha256:f1b13e4a1a796135900c354e20100dad7cbb264aab016b7b73e4d5eb9882073f

Observation bd10dc45-e6d9-413b-b4c2-10712ad8afb8 · outbound

This paper cites This isolates the impact of the architecture from the initialization scheme.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers This isolates the impact of the architecture from the initialization scheme

Reference 4

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Observation e7fcc269-84a2-4d5e-a514-8f5e1515621f · outbound

This paper cites Li, P., Yin, L., and Liu, S.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Li, P., Yin, L., and Liu, S

Reference 8

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source=pdf_text observed=2026-08-03T06:37:52.874760Z digest=sha256:06f64026423b48e1032b93fa48966fb2eeee97c315f83b460b04ef4f4ce0ec7e

Observation cc0dc7c0-85ff-46ac-a3ae-c82289339053 · outbound

This paper cites DeepSeek-V3 Technical Report.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers DeepSeek-V3 Technical Report

Reference 9

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source=pdf_text observed=2026-08-03T06:37:53.044794Z digest=sha256:04bf95bfebe8afb50647609577a0313768c4686d45fbf7c4a26441b03f36eb59

Observation 092d188c-22f4-48ec-aecf-9283e1ad4ed6 · outbound

This paper cites Spike No More: Stabilizing the Pre-training of Large Language Models.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Spike No More: Stabilizing the Pre-training of Large Language Models

Reference 12

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source=pdf_text observed=2026-08-03T06:37:53.414766Z digest=sha256:01198002a8ed5ac167841ac3f5f924f5980a3b3969cff81bc5f2eb2947e0ebc1

Observation 58e2e5ac-c5cc-49b1-b893-abb46450f71b · outbound

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

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Gemma 2: Improving Open Language Models at a Practical Size

Reference 13

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source=pdf_text observed=2026-08-03T06:37:53.551888Z digest=sha256:8bdc67e119ded5eef8808cf2469788475d1680240bca029416773acc957eb59f

Observation b0900031-97ac-4c3a-a19a-e25507a68749 · outbound

This paper cites L., Li, B., Lei, B., Wang, B., Rong, B., Wang, C., Zhang, C., Gao, C., Zhang, C., Sun, C., et al.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers L., Li, B., Lei, B., Wang, B., Rong, B., Wang, C., Zhang, C., Gao, C., Zhang, C., Sun, C., et al

Reference 14

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source=pdf_text observed=2026-08-03T06:37:53.644874Z digest=sha256:69c72e36a398fc16c7b560f86e0395e67a78c602d6bc49b900df4d161f082381

Observation b6ea308e-b69c-4a77-896f-cb6f09727799 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers LLaMA: Open and Efficient Foundation Language Models

Reference 15

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source=pdf_text observed=2026-08-03T06:37:53.764764Z digest=sha256:ebe261bd3699acea6a4b6fd503262f8a3b1be5f493da63e3e46b5acf7972a6e1

Observation 3238ff96-6bf5-48e8-8794-a9cf4ac718ef · outbound

This paper cites Qwen3 Technical Report.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Qwen3 Technical Report

Reference 16

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source=pdf_text observed=2026-08-03T06:37:53.934754Z digest=sha256:a6a908b4338252e1423cda9235deff403525061b9ff4d2e4bab94f45005e016c

Observation e6dce9cb-04ad-4ae2-85e6-515d2827fca3 · outbound

This paper cites Improving deep transformer with depth-scaled initialization and merged attention.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Improving deep transformer with depth-scaled initialization and merged attention

Reference 17

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Observation 7399ff37-2bdb-439e-919d-6b77bb2df8cb · outbound

This paper cites 10 SpanNorm: Reconciling Training Stability and Performance in Deep Transformers A.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers 10 SpanNorm: Reconciling Training Stability and Performance in Deep Transformers A

Reference 18

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Observation 9c50c541-1c22-4056-8cee-43e8bfb14b7e · outbound

This paper cites an unresolved cited work.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-03T06:37:54.376442Z digest=sha256:7ddfea916096c9493fffae6f7fea1b688e88f9384b6acc43660d37a8eb8f54ae

Observation 302ab2e2-82a8-401b-87c3-80f181cbb33a · outbound

This paper cites In contrast, SpanNorm demonstrates superior representational capability.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers In contrast, SpanNorm demonstrates superior representational capability

Reference 22

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source=pdf_text observed=2026-08-03T06:37:54.684811Z digest=sha256:46da73a06648d402501b27120e81b61e374cd700833692c77ba0b66aecffbf60

Observation cfe41fac-244a-41c7-ae4f-97059b847ab6 · outbound

This paper cites All models are trained on the same subset of the SlimPajama dataset (from 30B to 200B) with the Mistral tokenizer Jiang et al.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers All models are trained on the same subset of the SlimPajama dataset (from 30B to 200B) with the Mistral tokenizer Jiang et al

Reference 24

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source=pdf_text observed=2026-08-03T06:37:54.934759Z digest=sha256:793783f37bfcf6c5fc80ad899043902b3fe3de6b946fe794beedfc03aa32e54f

Observation db33dd57-8bab-4de8-8000-12cb264d854e · outbound

This paper cites an unresolved cited work.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-03T06:37:55.054757Z digest=sha256:11b15e74e9663e13b218ea7993a32a21905d9d8ff8b623238100174d9ba9f036

Observation 103a586a-32d8-4e97-b0bf-b6b0082c4c3c · outbound

This paper cites It employs an MLA architecture, activating 6 out of 64 experts.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers It employs an MLA architecture, activating 6 out of 64 experts

Reference 64

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Observation e8aeaa3c-e490-4587-a8e8-cf716e482242 · outbound

This paper cites SpanNorm shows a strict monotonic decrease in training loss, confirming robust stability and the effective avoidance of depth degradation at extreme scales.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers SpanNorm shows a strict monotonic decrease in training loss, confirming robust stability and the effective avoidance of depth degradation at extreme scales

Reference 384

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Observation 35c1985e-c86e-49eb-b860-1e88f053b8eb · outbound

This paper cites Shallow-to-deep training for neural ma- chine translation.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Shallow-to-deep training for neural ma- chine translation

Reference 2016

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Observation 39a1bcaa-cc6c-41a5-8c03-bec1ba88d064 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers An image is worth 16x16 words: Transformers for image recognition at scale

Reference 2020

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Observation 7b20b22d-d874-4795-bebe-a0d7a08359e0 · outbound

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

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers NormFormer: Improved Transformer Pretraining with Extra Normalization

Reference 2021

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Observation 5565be61-45ba-488c-a31d-e5f4c4449a3b · outbound

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

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models

Reference 2023

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source=pdf_text observed=2026-08-03T06:37:52.424760Z digest=sha256:adb90a47f57413d787e3f2e9bc8ebccf2c7d810581fb87c9da62c0dc0f850057

Observation 06bcf884-d9c7-4e6d-947d-a84f125f271c · outbound

This paper cites Peri-LN: Revisiting Normalization Layer in the Transformer Architecture.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Reference 2024

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Observation 3cfb67d6-0ef5-4585-a171-c128c013ec0c · outbound

This paper cites Mistral 7B.

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers Mistral 7B

Reference 2025

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source=pdf_text observed=2026-08-03T06:37:52.334759Z digest=sha256:321df94e5ae6054d9ed5aa0e0556911763ef7698f1656c73652f757df61f74cd

Pith citing papers

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

Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth cites this paper.

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 74a1add0-7c06-4ea3-add0-8eff061bd9e7 · inbound

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning cites this paper.

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning SpanNorm: Reconciling Training Stability and Performance in Deep Transformers

Reference 31

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