Paper Citation Record · LEDGER

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2504.12471.

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

pith.paper-citation-record.v1
2504.12471 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:01.600260Z

measured 46 of 46 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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy18
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5bc583a-1d18-4d07-b544-3cfbc9c63eca · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Source-reported events for the cited work

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Observation 04709667-ddbf-405f-a4bd-73688a44223d · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.383768Z

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Observation 02205f2c-1793-4a17-99bf-9f8710723541 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.389715Z

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Unavailable: canonical work link unavailable.

Observation d6e31786-ba41-4a6b-a1f7-3ff7ca54effd · outbound

This paper cites Xlnet: Generalized autoregressive pretraining for language understanding,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Xlnet: Generalized autoregressive pretraining for language understanding,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.394718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82e43c9f-4fc6-4ad5-ae42-b7508533dd6a · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.399645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a1ef966-eeb9-4a2e-b753-49bee8ac847e · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.404894Z

Source-reported events for the cited work

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Observation e3d99227-abc4-49eb-a972-63038d69d19e · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Tokens-to-token vit: Training vision transformers from scratch on imagenet,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.410614Z

Source-reported events for the cited work

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Observation 86087a9a-baec-4b2b-a6dc-ae24c91e7838 · outbound

This paper cites Train big, then compress: Rethinking model size for efficient training and inference of transformers,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Train big, then compress: Rethinking model size for efficient training and inference of transformers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.272089Z

Source-reported events for the cited work

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Observation 0e480de8-554a-4c8f-a501-7298829a108a · outbound

This paper cites Decoupled greedy learning of cnns,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Decoupled greedy learning of cnns,

Reference 9

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Source-reported events for the cited work

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Observation 4e8f5d6b-9443-41c8-bf3c-74974535856d · outbound

This paper cites Distributed learning of fully connected neural networks using indepen- dent subnet training,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Distributed learning of fully connected neural networks using indepen- dent subnet training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.246274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c023a108-ae6c-4b7d-8f26-6d9c6cc591b6 · outbound

This paper cites Decentralized training of foundation models in heterogeneous environments,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Decentralized training of foundation models in heterogeneous environments,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.230088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f041e34f-aae2-4916-9ead-f60fc8ea61d7 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 12

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Observation 6faf335d-8fd7-4e43-96fd-e2b04e8c3269 · outbound

This paper cites GSPMD: General and Scalable Parallelization for ML Computation Graphs.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models GSPMD: General and Scalable Parallelization for ML Computation Graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.439349Z

Source-reported events for the cited work

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Observation ccaef75d-83c5-4b45-8cec-db44cf6b1fc8 · outbound

This paper cites An efficient 2d method for training super-large deep learning models,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models An efficient 2d method for training super-large deep learning models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.214409Z

Source-reported events for the cited work

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Observation 4320ec30-7210-40a5-a002-fee5d1716e03 · outbound

This paper cites Maximizing Parallelism in Distributed Training for Huge Neural Networks.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Maximizing Parallelism in Distributed Training for Huge Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.449128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b9a9a7-10df-4ba2-ae0c-5e966e4f8c9b · outbound

This paper cites Attention is all you need,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Attention is all you need,

Reference 16

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Source-reported events for the cited work

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Observation 56549b7d-b47f-46af-a1bc-77c70bf23c34 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.458824Z

Source-reported events for the cited work

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Observation eb481c18-cf18-4341-ad44-d177df2609b1 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.463962Z

Source-reported events for the cited work

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Observation 4dc69d1d-4ee9-45f5-9ddb-999c73ec0a71 · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Parameter-efficient transfer learning for nlp,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.469195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9f12e04-89a8-4707-a8c8-a7cea08d65a3 · outbound

This paper cites Transformer in transformer,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Transformer in transformer,

Reference 20

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Source-reported events for the cited work

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Observation eeb14255-c24c-408c-8437-a2b1da53971e · outbound

This paper cites Dynamic Model Pruning with Feedback.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Dynamic Model Pruning with Feedback

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ab2105b-d984-4bf5-a5f1-82535f07fb4e · outbound

This paper cites Single-shot pruning for pre-trained models: Rethinking the importance of magnitude pruning,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Single-shot pruning for pre-trained models: Rethinking the importance of magnitude pruning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.168168Z

Source-reported events for the cited work

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Observation 175af5f5-08f8-440a-92db-bd0b027f9159 · outbound

This paper cites Resource- efficient transformer pruning for finetuning of large models,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Resource- efficient transformer pruning for finetuning of large models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.150004Z

Source-reported events for the cited work

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Observation a8d6db2d-79ac-45cb-ba58-e75a61e57f95 · outbound

This paper cites Martello and P.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Martello and P

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.133939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 10bfffd0-03f8-4671-b402-0ab5a23e6dd0 · outbound

This paper cites A class of generalized greedy algorithms for the multi-knapsack problem,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models A class of generalized greedy algorithms for the multi-knapsack problem,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.118280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33ba2ba8-7552-4e28-a629-1a4b909625ba · outbound

This paper cites Dynamic programming revisited: Improving knapsack algorithms,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Dynamic programming revisited: Improving knapsack algorithms,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.102410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56e54159-64e2-4182-ac57-1cf760711725 · outbound

This paper cites An approximate dynamic programming ap- proach to multidimensional knapsack problems,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models An approximate dynamic programming ap- proach to multidimensional knapsack problems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.087031Z

Source-reported events for the cited work

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Observation 894fd2b9-442b-47c7-a8bc-b9890dacd224 · outbound

This paper cites Pytorch image models,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Pytorch image models,

Reference 28

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Observation 85a95a5f-ce58-4558-9e6a-e90d1ab14813 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Pytorch: An imperative style, high-performance deep learning library,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.060041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71ca0b38-9469-4d83-9c36-cc4b77b0b685 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 30

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Source-reported events for the cited work

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Observation bcab0ed5-41ea-4fbb-9e57-1557970f507c · outbound

This paper cites Where to pay attention in sparse training for feature selection?.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Where to pay attention in sparse training for feature selection?

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.044084Z

Source-reported events for the cited work

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Observation 5ef3de64-789e-440e-8201-99704436b26f · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 32

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Observation bb564558-b981-491a-8465-66bf03934ff4 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.536230Z

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Observation 7bc1e12c-f978-4af8-a3a0-f45e3d1e71a4 · outbound

This paper cites Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.540973Z

Source-reported events for the cited work

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Observation 0c8373a6-2710-4d9c-ad0b-523a863bc711 · outbound

This paper cites M6-T: Exploring Sparse Expert Models and Beyond.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models M6-T: Exploring Sparse Expert Models and Beyond

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.546388Z

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Observation aee6c30a-1aff-4e7b-9b8a-135de7eab909 · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.551591Z

Source-reported events for the cited work

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Observation 0050a150-c160-4e2e-b3ed-daaa81272531 · outbound

This paper cites Hash layers for large sparse models,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Hash layers for large sparse models,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:02.006695Z

Source-reported events for the cited work

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Observation 2fdb58dd-8b11-4296-a9a1-d46c85de0cc2 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.561592Z

Source-reported events for the cited work

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Observation 691ce3dc-2c8d-4d70-971d-cd7ec6a0d0c9 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.566591Z

Source-reported events for the cited work

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Observation 87a996e3-6500-4902-9882-304c8bec13aa · outbound

This paper cites Learning both weights and con- nections for efficient neural network,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Learning both weights and con- nections for efficient neural network,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.571670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2f50ac5-1285-4a8d-b766-85a488798fb9 · outbound

This paper cites Dynamic network surgery for efficient dnns,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Dynamic network surgery for efficient dnns,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:01.978419Z

Source-reported events for the cited work

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Observation 1d97bb73-1bef-4b96-a880-e413ea0a24fc · outbound

This paper cites Compression-aware training of deep networks,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Compression-aware training of deep networks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:01.962721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 758b4354-cabb-4fbd-b152-cb64229f978a · outbound

This paper cites “learning-compression.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models “learning-compression

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:01.947063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49c00761-95bd-49c7-88a0-208a1f28b007 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.590570Z

Source-reported events for the cited work

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Observation 02b0f596-11a5-4078-b95f-c9a752d8a809 · outbound

This paper cites Efficient lottery ticket finding: Less data is more,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Efficient lottery ticket finding: Less data is more,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:01.930852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2601157b-698e-4da9-97c2-fcb0decdc4d9 · outbound

This paper cites The lottery ticket hypothesis for object recognition,.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models The lottery ticket hypothesis for object recognition,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:01.914443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.