Paper Citation Record · LEDGER

DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2402.02622.

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

pith.paper-citation-record.v1
2402.02622 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:27:23.931619Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:16:23.506342Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ebe7aba1-6590-450a-9732-4e7ba3053499 · inbound

DeepCrossAttention: Supercharging Transformer Residual Connections cites this paper.

DeepCrossAttention: Supercharging Transformer Residual Connections DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T14:27:23.931619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 752dd56c-7ae3-4437-a519-c162639bf238 · inbound

Attention Residuals cites this paper.

Attention Residuals DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:39:04.527752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e996284c-af5d-46c1-a6b9-c36978271080 · inbound

Hyperloop Transformers cites this paper.

Hyperloop Transformers DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:16:04.630184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 75e1256a-7ba1-43e9-a2ad-cca353f2b269 · inbound

Delta Attention Residuals cites this paper.

Delta Attention Residuals DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:01.846287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e35a331c-b5c0-40a3-b5b0-3864b1304abb · inbound

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor cites this paper.

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:19:42.045199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9c897049-7f33-41b4-881d-c980320226bc · inbound

Do Value Vectors in Deep Layers Need Context from the Residual Stream? cites this paper.

Do Value Vectors in Deep Layers Need Context from the Residual Stream? DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:16:23.508188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a6a74708-5024-4255-a3e0-394c955f0a1c · inbound

Do Value Vectors in Deep Layers Need Context from the Residual Stream? cites this paper.

Do Value Vectors in Deep Layers Need Context from the Residual Stream? DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T12:41:22.235547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c577156-91b8-41dd-8fcb-6366b8867c76 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T11:39:23.058737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88939309-ecc3-4dce-96b2-19a208537475 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-04T01:39:48.022554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.