Paper Citation Record · LEDGER

Reducing Transformer Depth on Demand with Structured Dropout

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:1909.11556.

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

pith.paper-citation-record.v1
1909.11556 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:17.834796Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

273
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e1ad9f5c-7c94-4fca-b410-26e8483eec12 · inbound

PyTorch Distributed: Experiences on Accelerating Data Parallel Training cites this paper.

PyTorch Distributed: Experiences on Accelerating Data Parallel Training Reducing Transformer Depth on Demand with Structured Dropout

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-17T19:15:26.852762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 686627a5-f0e5-4e24-a436-285b6556240f · inbound

Eliciting Latent Predictions from Transformers with the Tuned Lens cites this paper.

Eliciting Latent Predictions from Transformers with the Tuned Lens Reducing Transformer Depth on Demand with Structured Dropout

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-12T16:54:37.594176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a778857-1a0e-42dd-9422-7548c2e75db6 · inbound

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach cites this paper.

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach Reducing Transformer Depth on Demand with Structured Dropout

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:39:41.141062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ac5c55d-a864-4dac-b941-0025254eda76 · inbound

AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformer cites this paper.

AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformer Reducing Transformer Depth on Demand with Structured Dropout

Reference 16

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Source-reported events for the cited work

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Observation 211c76de-123e-4481-a77a-df405572e788 · inbound

Position: The Future of Bayesian Prediction Is Prior-Fitted cites this paper.

Position: The Future of Bayesian Prediction Is Prior-Fitted Reducing Transformer Depth on Demand with Structured Dropout

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:36.354788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3223e1a1-e09b-4363-b4b1-f661be8cbb40 · inbound

Learning to Skip the Middle Layers of Transformers cites this paper.

Learning to Skip the Middle Layers of Transformers Reducing Transformer Depth on Demand with Structured Dropout

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:40:53.679247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 282d4541-9bd4-46a8-b938-768d71b1ce3b · inbound

Towards Universal & Efficient Model Compression via Exponential Torque Pruning cites this paper.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning Reducing Transformer Depth on Demand with Structured Dropout

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:42.899383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54e80b2d-9a4e-45cd-b894-d3ffe7f781a1 · inbound

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs cites this paper.

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs Reducing Transformer Depth on Demand with Structured Dropout

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:19.194766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec931f3a-5d9b-4e1a-b351-2bc27cd39f80 · inbound

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling cites this paper.

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling Reducing Transformer Depth on Demand with Structured Dropout

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:51.447217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3776073e-b025-4559-a80c-f2d61610c801 · inbound

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Reducing Transformer Depth on Demand with Structured Dropout

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.726762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b220cf03-cacc-40ac-99fe-602821af9ace · inbound

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code cites this paper.

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code Reducing Transformer Depth on Demand with Structured Dropout

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-19T00:01:55.897855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e4d35bd6-cf40-4be1-9924-1ff35b2f3be8 · inbound

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models cites this paper.

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models Reducing Transformer Depth on Demand with Structured Dropout

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-19T00:36:56.313975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b34d409-72f4-470e-8e2e-b23b693ea384 · inbound

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models cites this paper.

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models Reducing Transformer Depth on Demand with Structured Dropout

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-06T00:02:58.946950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1beebdf1-25db-475a-9bc5-eb1303dad0e5 · inbound

Harnessing Input-Adaptive Inference for Efficient VLN cites this paper.

Harnessing Input-Adaptive Inference for Efficient VLN Reducing Transformer Depth on Demand with Structured Dropout

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:16:22.237618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 483f8e97-ee2b-4ef9-8ae3-c0b17994d552 · inbound

VISP: Volatility Informed Stochastic Projection for Adaptive Regularization cites this paper.

VISP: Volatility Informed Stochastic Projection for Adaptive Regularization Reducing Transformer Depth on Demand with Structured Dropout

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T12:07:37.008912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4346a0-e7d6-4651-ba8f-7c985a2c2f7b · inbound

Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures cites this paper.

Aletheia: Gradient-Guided Layer Selection for Efficient LoRA Fine-Tuning Across Architectures Reducing Transformer Depth on Demand with Structured Dropout

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:38:07.556104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1ac1b49-3e8e-4b59-a63e-c429923851db · inbound

Depth Adaptive Efficient Visual Autoregressive Modeling cites this paper.

Depth Adaptive Efficient Visual Autoregressive Modeling Reducing Transformer Depth on Demand with Structured Dropout

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:26:27.625427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c01b4b4a-0906-42c7-af38-7334de1511bf · inbound

Language models recognize dropout and Gaussian noise applied to their activations cites this paper.

Language models recognize dropout and Gaussian noise applied to their activations Reducing Transformer Depth on Demand with Structured Dropout

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:41:02.186788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7351aec5-c8c2-4627-b739-ae141d653acf · inbound

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing cites this paper.

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing Reducing Transformer Depth on Demand with Structured Dropout

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T19:58:12.041938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67ff6be4-e9a7-4148-b7d1-cbc5aefda9fb · inbound

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency cites this paper.

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency Reducing Transformer Depth on Demand with Structured Dropout

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:56:14.586000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf2da7a7-a8d6-447c-a3d8-8ceccf243a33 · inbound

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations cites this paper.

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations Reducing Transformer Depth on Demand with Structured Dropout

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:51:29.267468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c13c2308-d5ae-45b4-981d-07a7b3f47ed0 · inbound

Shallow Prefill, Deep Decoding: Efficient Long-Context Inference via Layer-Asymmetric KV Visibility cites this paper.

Shallow Prefill, Deep Decoding: Efficient Long-Context Inference via Layer-Asymmetric KV Visibility Reducing Transformer Depth on Demand with Structured Dropout

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:01:09.011862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e9c90031-af5b-441b-9322-bd19b77d0272 · inbound

Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs cites this paper.

Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs Reducing Transformer Depth on Demand with Structured Dropout

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:58:04.030648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 972ad1df-c680-4ad2-807f-6c3adf3cb646 · inbound

Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility cites this paper.

Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility Reducing Transformer Depth on Demand with Structured Dropout

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:39:47.975021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a9ae38b3-1ea4-455c-add4-905c249bdbf4 · inbound

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs cites this paper.

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs Reducing Transformer Depth on Demand with Structured Dropout

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:36:57.370073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7cada08f-0e51-4b6a-b246-8e3b26a7c03d · inbound

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation cites this paper.

Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation Reducing Transformer Depth on Demand with Structured Dropout

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-27T16:41:03.364124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 557c46b0-6da5-4ea7-a4ed-dee2dfdb2bb1 · inbound

Tapered Language Models cites this paper.

Tapered Language Models Reducing Transformer Depth on Demand with Structured Dropout

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.954130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23ae4396-631b-4c90-a1cd-57c22b8282ba · inbound

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping cites this paper.

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping Reducing Transformer Depth on Demand with Structured Dropout

Reference 34

Resolution: malformed identifier; arxiv_id, observed 2026-06-30T07:34:21.633951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee248a96-9780-4b07-b3fe-04d7b3b56d60 · inbound

Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study cites this paper.

Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study Reducing Transformer Depth on Demand with Structured Dropout

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T18:20:38.521564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.