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Paper Citation Record · LEDGER

Decentralized Low-Rank Fine-Tuning of Large Language Models

As of 12 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 2 inbound Pith citation observations for arXiv:2501.15361.

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

pith.paper-citation-record.v1
2501.15361 v5

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:48.395288Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:42:45.995786Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:49:44.470502Z

Reference resolution

85 of 85 outbound references displayed

  • verified exact2
  • verified fuzzy37
  • unresolved46
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dc2c9e3a-0db4-48b5-b407-34d69d74d7a7 · outbound

This paper cites GPT-4 Technical Report.

Decentralized Low-Rank Fine-Tuning of Large Language Models GPT-4 Technical Report

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:47.826602Z digest=sha256:91be8fd84f3faa7a49123e496d33d9b749ce90c40f44a486479438f9c1cb489a

Observation c6e2396a-c779-4b9b-9459-5c48b2181fb1 · outbound

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

Decentralized Low-Rank Fine-Tuning of Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 2

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source=pdf_text observed=2026-08-10T14:26:47.834508Z digest=sha256:9d10102031ef8f72fce3983d8ede423d1c48a2ba32c369b017b5e3a40f34deb9

Observation 3b5960cc-8c2a-4f79-95e5-405a3b7ec608 · outbound

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

Decentralized Low-Rank Fine-Tuning of Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

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source=pdf_text observed=2026-08-10T14:26:47.840098Z digest=sha256:288c7d81e72335a7b9ea753d28cd31d5430fd0d9174e14f8da2b7ecd0b8e0eb0

Observation 79506c56-8e66-4c0e-9eed-45760d8bc04a · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Decentralized Low-Rank Fine-Tuning of Large Language Models On the Opportunities and Risks of Foundation Models

Reference 4

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source=pdf_text observed=2026-08-10T14:26:47.845767Z digest=sha256:efb88073f492f2bf0f06b8401bf2e3dfe3be95ca98474ae8dc21042cb7cc5bef

Observation 99a1a65e-d697-4146-a801-3d463117de9c · outbound

This paper cites Attention is all you need,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Attention is all you need,

Reference 5

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source=pdf_text observed=2026-08-10T14:26:47.857500Z digest=sha256:43c3d7ea3aabbc5c19cebc1b1b703f3796e8cad83b4b277d3078416539d55f51

Observation bac3bc96-d5e5-4304-8901-df7f974485db · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Decentralized Low-Rank Fine-Tuning of Large Language Models Universal Language Model Fine-tuning for Text Classification

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:47.863695Z digest=sha256:4a5ac383ee5a4aae595543988167b0055b78237633b95215fec88293711757f1

Observation 247ab932-610f-42b4-a19c-002d23aaa9ce · outbound

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

Decentralized Low-Rank Fine-Tuning of Large Language Models Parameter-efficient transfer learning for nlp,

Reference 7

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source=pdf_text observed=2026-08-10T14:26:47.870695Z digest=sha256:1b8c0849a524d12f8ffa24e46dc686a341b39609877b2908f18ffdd298c6dc04

Observation 3940cc1c-bc24-44fc-8a3a-18198529be2d · outbound

This paper cites The power of scale for parameter-efficient prompt tuning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models The power of scale for parameter-efficient prompt tuning,

Reference 8

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source=pdf_text observed=2026-08-10T14:26:47.877168Z digest=sha256:2944c92a70e2d81f179a58348358759ce077a296ce20725af99acee61529a945

Observation 79579a02-fd6c-44e7-b4d9-fcee0d09913d · outbound

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

Decentralized Low-Rank Fine-Tuning of Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 9

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source=pdf_text observed=2026-08-10T14:26:47.882386Z digest=sha256:2630b6ff0483aae75af36d816170ddd29cd4f8dac910af8c69fd0f1f7a307d07

Observation 4fb7e7d1-541a-410b-bfe4-f5c1db762c98 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Parameter-efficient fine-tuning of large-scale pre-trained language models,

Reference 10

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source=pdf_text observed=2026-08-10T14:26:47.896874Z digest=sha256:7f1973b2043eebdcaf582904ce793f8b0845a21f5d72801a20b167b0322feb0c

Observation 546f6a61-ff0d-43ad-8759-6b9bd48253dd · outbound

This paper cites Federated multilingual models for medical transcript analysis,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated multilingual models for medical transcript analysis,

Reference 11

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source=pdf_text observed=2026-08-10T14:26:47.905032Z digest=sha256:ed7234b644bb5a54ba2fc5dace455618025f08e36d3859b5e432d87b329496fa

Observation 407df575-2128-4443-abe1-788bf2ea6e9f · outbound

This paper cites Federated Learning of Medical Concepts Embedding using BEHRT.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated Learning of Medical Concepts Embedding using BEHRT

Reference 12

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local_arxiv, observed 2026-08-10T14:26:49.287656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:47.910511Z digest=sha256:dec9a568eae29460cf1fb2fac80e17292d3e870703d5447d6b6209eed13ba67e

Observation ab818ad0-ad5c-49b8-8103-9a8622293da7 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Communication-efficient learning of deep networks from decentralized data,

Reference 13

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source=pdf_text observed=2026-08-10T14:26:47.916476Z digest=sha256:3d8e5f8306fe40508d16f1eeddac348cc2483848c4db38aee90230fc7a9b154e

Observation b9867b81-ffc7-4a29-b54c-c0b6b79383aa · outbound

This paper cites Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models,

Reference 14

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raw_fallback, observed 2026-08-10T14:26:50.167832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:47.924071Z digest=sha256:928373d18930cd0e5a1f1a5cbc810f185fef7e30fa2a8e5cde3f256404b888bb

Observation a727df8b-2132-41f1-8dfa-e4199733ad60 · outbound

This paper cites FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

Decentralized Low-Rank Fine-Tuning of Large Language Models FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 15

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source=pdf_text observed=2026-08-10T14:26:47.930606Z digest=sha256:cbe1e8e185e38516e8865e55d97378ea0d675cb87a84a76b609d2d48e197d343

Observation 80c4fd20-ac24-42df-9454-cdaf49f164cf · outbound

This paper cites Fedprompt: Communication-efficient and privacy- preserving prompt tuning in federated learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Fedprompt: Communication-efficient and privacy- preserving prompt tuning in federated learning,

Reference 16

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raw_fallback, observed 2026-08-10T14:26:50.145393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:47.937265Z digest=sha256:aa0ae04fc43983c038e7436864ff71bea5a7c1929f67d839d809a340a6b99040

Observation c30afea2-c4a5-412b-a2fc-2176033e1ee2 · outbound

This paper cites Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models.

Decentralized Low-Rank Fine-Tuning of Large Language Models Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models

Reference 17

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local_arxiv, observed 2026-08-10T14:26:49.246403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:47.945834Z digest=sha256:4df5e17bb62f1e24a509d70c7cfb5a010e8911607f9c0ad98b54b6a586aaa69a

Observation 954dc0b7-9727-4ff5-97c8-fc042d3dee23 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

Decentralized Low-Rank Fine-Tuning of Large Language Models SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 18

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source=pdf_text observed=2026-08-10T14:26:47.951941Z digest=sha256:e456ab832c9c2f054e8db7d5bb2a0680996f638ce68006d6548bbf3b1fdd7836

Observation 62b003e6-dd0e-4a66-bc62-886f563e4611 · outbound

This paper cites FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition.

Decentralized Low-Rank Fine-Tuning of Large Language Models FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

Reference 19

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source=pdf_text observed=2026-08-10T14:26:47.958439Z digest=sha256:e26a074f62143a1a8e12d1bc9288ba203adbda1dcc4ee086027c427afcfe968d

Observation cbbeede9-7d39-48bb-8ad4-e552ef5af637 · outbound

This paper cites Heterogeneous lora for federated fine-tuning of on-device foundation models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Heterogeneous lora for federated fine-tuning of on-device foundation models,

Reference 20

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raw_fallback, observed 2026-08-10T14:26:50.120085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:47.963999Z digest=sha256:be0c0bd2b267dc434516f5e55f3647916ce0fbb7ef7fbbaa55f2e9be3ae10c32

Observation 517a7ba1-f2a3-4908-b66b-aaa996803aa3 · outbound

This paper cites Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 21

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source=pdf_text observed=2026-08-10T14:26:47.973305Z digest=sha256:e4c461e484b316496e836d0e43feddb77fc48e99ad0c00ec56099b5989b310e4

Observation 34a600be-a888-4b0b-9c1c-69a8e3a594d3 · outbound

This paper cites FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

Decentralized Low-Rank Fine-Tuning of Large Language Models FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 22

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source=pdf_text observed=2026-08-10T14:26:47.988265Z digest=sha256:5cbfdc23af992cace78f8d63f35dd64ca6437dcc6a6c08e0f17cb20401dc8638

Observation cacfa445-76ae-4eee-9533-2c00368257f4 · outbound

This paper cites Federated LoRA with Sparse Communication.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated LoRA with Sparse Communication

Reference 23

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source=pdf_text observed=2026-08-10T14:26:47.994182Z digest=sha256:3cf478b79eacb86e231469eb9cdcf5fa5cc283598485ec7471150bcd6c2fb4fc

Observation 11fa83cd-1f74-428f-a42e-5a0a638dacbf · outbound

This paper cites Improving LoRA in Privacy-preserving Federated Learning.

Decentralized Low-Rank Fine-Tuning of Large Language Models Improving LoRA in Privacy-preserving Federated Learning

Reference 24

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source=pdf_text observed=2026-08-10T14:26:48.001154Z digest=sha256:21bd9f428c096e83de072c076d4db6346d862447c8d98bd68c71becc03d56bee

Observation 15a50620-2670-43d6-9305-e727fa34264c · outbound

This paper cites Robust federated finetuning of foundation models via alternating minimization of LoRA,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Robust federated finetuning of foundation models via alternating minimization of LoRA,

Reference 25

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raw_fallback, observed 2026-08-10T14:26:50.100228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.007824Z digest=sha256:7ecebed1e2601704cb6d11993c4f5386c38e5d54b3c7ecb41ca0c2eb18c69fbc

Observation d2fd41b2-ce75-4429-920b-34b5ec644afe · outbound

This paper cites Decentralized federated learning: A survey and perspective,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Decentralized federated learning: A survey and perspective,

Reference 26

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raw_fallback, observed 2026-08-10T14:26:50.077727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.013455Z digest=sha256:1805e5bbc3cf9502716568d310f2253162d67a20018e66f37eecf558fc8054d6

Observation fb2e621d-6fa2-4c90-8139-051aba86a2a2 · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Decentralized Low-Rank Fine-Tuning of Large Language Models Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 27

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source=pdf_text observed=2026-08-10T14:26:48.018853Z digest=sha256:24a8ee852b3428ae360ff0748b4433d19cccf92f2bb49ee3da217e7e21b84f79

Observation 7e5e5d2a-f582-4bab-b451-44f8999937ff · outbound

This paper cites Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Scalable multi-robot collaboration with large language models: Centralized or decentralized systems?,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:50.056936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.024191Z digest=sha256:7eb0fbbe63335293860999c3c9a8661ac65811dba709e8f913e5dcb52d1fbf1e

Observation ac378c11-460c-48da-8118-fa4accef2f4c · outbound

This paper cites Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-10T14:26:50.038631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.033410Z digest=sha256:6a4a7140e806f6470c99487138f8e15a768f1b92f70e8df51a638fc907ae281e

Observation a4d6dc44-8bdf-42c6-a638-a5947babc93e · outbound

This paper cites Personalized Federated Learning: A Meta-Learning Approach.

Decentralized Low-Rank Fine-Tuning of Large Language Models Personalized Federated Learning: A Meta-Learning Approach

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.038804Z digest=sha256:22a9bcec6c69b6a1fedd0c1326203d61ab017cc682f0454e023537931004d5e4

Observation 30a8dcb6-a53d-4dea-8a2a-b767e2af4d0a · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimization,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Tackling the objective inconsistency problem in heterogeneous federated optimization,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-10T14:26:50.019778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.046119Z digest=sha256:2e9da27ae45ba0e5bee7903edd07e7a90026d74b2fb684529ecc466e1608d9a9

Observation 257c1c24-e455-4a0c-95df-ce7235741c50 · outbound

This paper cites Accelerating gossip sgd with periodic global averaging,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Accelerating gossip sgd with periodic global averaging,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.998265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.054257Z digest=sha256:4bdd7be339df64a9d9415d9ad6667dd12e6a7e02c3e9ed9bbffddb56f8fa714f

Observation 5ae05b39-8440-4899-9b25-7ce8ad3af3ff · outbound

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

Decentralized Low-Rank Fine-Tuning of Large Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 33

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.059842Z digest=sha256:906ffc973591eab96b3179b85ccf9d68eee6cf48a1a0aadcf1161a22e284f083

Observation aaec8657-726c-4e8b-bd80-42cc9efe334d · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,

Reference 34

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source=pdf_text observed=2026-08-10T14:26:48.069205Z digest=sha256:027f033016c9bd9498fc93f5b33b41853c3a76cf79b91c831d47e620c8b43a00

Observation ab55290e-a8f6-4d7f-afcc-1bb1e719ff3c · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,

Reference 35

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source=pdf_text observed=2026-08-10T14:26:48.074803Z digest=sha256:1cce60574286228152cea9dcb2e5035524b3bc08a986b34b53aef3d2769cf42a

Observation 0c500fa2-d663-4cc4-87c4-a945857bd0da · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods.

Decentralized Low-Rank Fine-Tuning of Large Language Models Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.962147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.083120Z digest=sha256:f32ec3eb3bc65b907b466c5b704cccac2c585229111051e21f882716fb78740c

Observation 791dd9ec-ba10-4b69-8829-aeadcce217ab · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Decentralized Low-Rank Fine-Tuning of Large Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 37

Resolution
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no resolver link, observed 2026-08-10T14:26:48.088192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.088192Z digest=sha256:0871160d69a32d8901e90d29b82600f31901c65d5731913d7b50bd83ccb4717a

Observation 66eb5261-1253-4543-ab8b-237fa78a61ed · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Qlora: Efficient finetuning of quantized llms,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.942988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.094677Z digest=sha256:d54e8d30866a7a402d707c0986af80c8a91ba387b566c2c68711ca73631890fd

Observation 52393fe7-cc86-4363-98f7-c9e6ffb49cbc · outbound

This paper cites The non-iid data quagmire of decentralized machine learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models The non-iid data quagmire of decentralized machine learning,

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.099911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.099911Z digest=sha256:e16e0674bb88006dc05c8a1f3df58aa478b6c093a656a271c679924e68fbe4a3

Observation baeca6bd-bc5b-4273-82bb-81ea9f70c11f · outbound

This paper cites Federated optimization in heterogeneous networks,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated optimization in heterogeneous networks,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.907437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.106427Z digest=sha256:9f05830cca10957b4e9fded57c9ded834b07f732bb6070dbde7032313b886f9d

Observation 98a26d19-f8e6-4c37-abc8-c5e46a14a409 · outbound

This paper cites Superglue: Astickierbenchmarkforgeneral-purposelanguageunderstandingsystems,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Superglue: Astickierbenchmarkforgeneral-purposelanguageunderstandingsystems,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.890721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.112343Z digest=sha256:13b791400bdeacda510dab504eb8251b16fdfce10c91a4256558bbb242602523

Observation 430fb60e-8b27-4c05-9cfd-6bd1c422d396 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Decentralized Low-Rank Fine-Tuning of Large Language Models SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.118029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.118029Z digest=sha256:ad56a92b58d44c316c313f56046fea8f911b74a0e9d208fd70906c0c941f4ad9

Observation 901957fc-2be0-466e-9018-a4e9b2156a40 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Decentralized Low-Rank Fine-Tuning of Large Language Models DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.125084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.125084Z digest=sha256:cc925d51370ab85252517f9e12f814bb8bd82a40c42ddb5520bcc645ca21b292

Observation a0fb72b2-1818-4ab7-a80f-2ab9c3b931f5 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Decentralized Low-Rank Fine-Tuning of Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.130074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.130074Z digest=sha256:bda9af1e7e40f9ea824ec9ae4ba45b30c73c017e3f8d2604119217021cd3f8d5

Observation d8d1f3ff-ae91-46e1-a451-c15fd09a0fcd · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Decentralized Low-Rank Fine-Tuning of Large Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.141568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.141568Z digest=sha256:5f0d9d7ecef8b767458c4005e027ab0d8d228a176bb155d66f374af81ecdabe2

Observation 14bc44b4-1f5a-4414-b5ab-5457dded965b · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

Decentralized Low-Rank Fine-Tuning of Large Language Models Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.147399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.147399Z digest=sha256:1c2ead50b201e817952803060a2c8081a74cd8a65231d90ee4d5cdab25cf8db4

Observation b974ad1c-6242-4e26-b075-b16aff599881 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Decentralized Low-Rank Fine-Tuning of Large Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.152943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.152943Z digest=sha256:d912dc7e406182ff382b371ae3cce328f40bbcfe0ba15f321944bb205bff3612

Observation 32eb53a0-98a8-40d0-8fab-c960d5a567f5 · outbound

This paper cites Parameter-Efficient Fine-Tuning without Introducing New Latency.

Decentralized Low-Rank Fine-Tuning of Large Language Models Parameter-Efficient Fine-Tuning without Introducing New Latency

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.160065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.160065Z digest=sha256:ecf9b67038c014cff329ba6b6732b227fd9aad530b390019320a3a908219395f

Observation 60365008-13ca-4818-8e8d-8349f57ddb64 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Decentralized Low-Rank Fine-Tuning of Large Language Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.167909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.167909Z digest=sha256:d3fc999b62c7a471e52b683e75e14d702e999679fe2be80a99a571db713e235c

Observation 9e0a58f6-96f4-43a1-94c2-341b6015e5b6 · outbound

This paper cites an unresolved cited work.

Decentralized Low-Rank Fine-Tuning of Large Language Models Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-10T14:26:49.874728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.173331Z digest=sha256:537562aaa8ce057e4d6c6095d8b363fd3868f1801ec7120ab93779e885cb14e9

Observation 9ab3f028-99ac-4c54-9649-2b4aab46ec48 · outbound

This paper cites Gossip-based computation of aggregate information,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Gossip-based computation of aggregate information,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.854267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.180130Z digest=sha256:c89f2d85020337b2d730ccbe3331503606489b3adae1c9272836ee60181f9ce3

Observation 0de25ad3-8f4e-478a-8830-ff2788c55351 · outbound

This paper cites Randomized gossip algorithms,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Randomized gossip algorithms,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.836568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.189332Z digest=sha256:eaebd98dda717564b3d8753eb84fee0cf64c1a8cd64dead7d79cbab268b9a103

Observation 8931c29f-b855-4b75-878f-c3ebbfb9bd8c · outbound

This paper cites Distributed subgradient methods for multi-agent optimization,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Distributed subgradient methods for multi-agent optimization,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.818957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.197278Z digest=sha256:421db2c6859020febdce18eff4f4a0ac432baf13c224e94f74ad72b79f1b9eec

Observation 13d63b38-64d9-491f-8c1d-d98a926522a9 · outbound

This paper cites A randomized incremental subgradient method for distributed optimization in networked systems,.

Decentralized Low-Rank Fine-Tuning of Large Language Models A randomized incremental subgradient method for distributed optimization in networked systems,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.802396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.204501Z digest=sha256:f9ec2eea91ba28c1cd7694cd1ada2118ff4b5c8a7d216cc99c0b84db9bc10834

Observation ae78c50b-88d6-471e-9c2c-e28038ac35f9 · outbound

This paper cites Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.786516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.211072Z digest=sha256:fc4a2e5a9fb60d0b19ea3d903b56af4b4f4b03c41e7068df87afe86e2abfb8bb

Observation ad71bf89-06ce-4e8f-903d-fb8f00a4c605 · outbound

This paper cites Swarm learning for decentralized and confidential clinical machine learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Swarm learning for decentralized and confidential clinical machine learning,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.769634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.216418Z digest=sha256:052a95acb1512fabc0007ac583a6b0777ef146ad1f1f32faf3ebe26f9b402a36

Observation 7a2fc0bd-a587-4a5f-ab03-92528e872ebc · outbound

This paper cites Decentralized federated learning for industrial iot with deep echo state networks,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Decentralized federated learning for industrial iot with deep echo state networks,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.752236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.222286Z digest=sha256:cf281f115d22f1cf777e82e7ca0de51c2af94afc7f9342f1699eec7b6cdb6546

Observation cf98733b-8bf1-4c0c-a936-8ba658eb7394 · outbound

This paper cites Iot with blockchain: A new infrastructure proposal,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Iot with blockchain: A new infrastructure proposal,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.737027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.227999Z digest=sha256:c494b7fc820a31c50cc0ef831beb90ef9d72e02f725376578c6e5d813ac16671

Observation a475e788-f5cc-4f12-bdf4-a200fb30f970 · outbound

This paper cites Network anomaly detection for iot using hyperdimensional computing on nsl-kdd,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Network anomaly detection for iot using hyperdimensional computing on nsl-kdd,

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.234417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.234417Z digest=sha256:e7724ec57d39daf3336d6b94710816e99bd8760169790de37e26d5349535326e

Observation 3beaeda1-4527-4bd3-abc0-a26d59ad5595 · outbound

This paper cites Spreadgnn: Decentralized multi-task federated learning for graph neural networks on molecular data,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Spreadgnn: Decentralized multi-task federated learning for graph neural networks on molecular data,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.718657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.240432Z digest=sha256:c0ecef0f16b78216f3966f2a165583dd9d2e14dfb30c9f570cb8f7555125c308

Observation 8dba3402-3cc7-4285-bafc-d4726c622212 · outbound

This paper cites Lora training in the ntk regime has no spurious local minima,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Lora training in the ntk regime has no spurious local minima,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.697664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.247045Z digest=sha256:d6354d27a22109b447ced05a4334f27b2961dc729f95d25da85f8832f1c361ad

Observation 4b9bc3a0-281c-4e72-bf22-4e0736ca3f5c · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Neural tangent kernel: Convergence and generalization in neural networks,

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.253307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.253307Z digest=sha256:ff9dd82a9b0037adf6a7e53ed6cc7092e17cf069e37125e0f510a733db0e6b57

Observation ed76ec4e-2c92-417a-96d2-e8554a2ed39d · outbound

This paper cites Asymmetry in low-rank adapters of foundation models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Asymmetry in low-rank adapters of foundation models,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.671364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.259224Z digest=sha256:dc8cb6bc226ac315a30f636367fd39a3b016af66cdbb40860dbae1f8f3a8f1fa

Observation 3eb0d64b-4891-4e21-aa78-e493ce0d0a48 · outbound

This paper cites Gradient dynamics for low-rank fine-tuning beyond kernels.

Decentralized Low-Rank Fine-Tuning of Large Language Models Gradient dynamics for low-rank fine-tuning beyond kernels

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.265673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.265673Z digest=sha256:6722e359e5f19ec466512d1ea1179d487a1d8215c8ff823c6e10442e41dd62e7

Observation 1ca40c4e-bcd7-4992-adfa-2d371f10600a · outbound

This paper cites Implicit balancing and regularization: Generalization and convergence guarantees for overparameterized asymmetric matrix sensing,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Implicit balancing and regularization: Generalization and convergence guarantees for overparameterized asymmetric matrix sensing,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.656530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.272135Z digest=sha256:5339651730f79985998a7fa97a5d2bfc05daf26109ee8f0ae6f145501cf669a3

Observation e0556160-fb1c-42aa-b04d-eb48db37835d · outbound

This paper cites On the crucial role of initialization for matrix factorization,.

Decentralized Low-Rank Fine-Tuning of Large Language Models On the crucial role of initialization for matrix factorization,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.640035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.279755Z digest=sha256:e89fcbb524db2a77e797d8d05c2ede3e2a152da14d44578b528bba96de8ef06c

Observation 40b1b03b-3966-4eb0-a99d-c160f9c3cbab · outbound

This paper cites LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently.

Decentralized Low-Rank Fine-Tuning of Large Language Models LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.286017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.286017Z digest=sha256:f7fbd5d7f3a02799022b90a57bc3070fcce440a6a1b82feb5106e14f83689865

Observation e0217aef-fee6-4b06-8bf9-f0c5fec41e52 · outbound

This paper cites Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation.

Decentralized Low-Rank Fine-Tuning of Large Language Models Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.291646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.291646Z digest=sha256:5197e20554f0cce5e427bebe5c2949a9b4dd9c85b4e0d1eb5bf9ca7a381ad99a

Observation 364e94af-74a4-46d8-91e0-4b78daa97f1a · outbound

This paper cites Selective aggregation for low-rank adaptation in federated learning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Selective aggregation for low-rank adaptation in federated learning,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.623085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.299188Z digest=sha256:709af90d9801798abe0c906b899d2c88918c7ccbbd28da1ed6ae49621147a342

Observation bc94762a-45c5-447b-a1e5-9b2f71dd42b1 · outbound

This paper cites Federated residual low-rank adaptation of large language models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated residual low-rank adaptation of large language models,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.605546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.305305Z digest=sha256:367f97ad95ada9b6b55fe90a241e88daf9f6dda89f2deb3c78ee4df8532df920

Observation 8db27b12-2541-4ef6-9ed2-fef66b5c34b3 · outbound

This paper cites FedEx-LoRA: Exact aggregation for federated and efficient fine-tuning of large language models,.

Decentralized Low-Rank Fine-Tuning of Large Language Models FedEx-LoRA: Exact aggregation for federated and efficient fine-tuning of large language models,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.589647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.311660Z digest=sha256:daee99b12e013057065eddc004893eb53a8aa2fc171ba69d2130e0fba2f27ee5

Observation 56c0a9db-93f6-4b63-9bf8-288050a385bd · outbound

This paper cites Towards robust and efficient federated low-rank adaptation with heterogeneous clients,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Towards robust and efficient federated low-rank adaptation with heterogeneous clients,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.574350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.317509Z digest=sha256:85093eff6ec4466e72b167022450f67edfc30dfa20df3442ae2d82d43c738e8d

Observation 54c58582-8544-4a2e-be81-eaebe25afed9 · outbound

This paper cites Fed-SB: A silver bullet for extreme communication efficiency and performance in (private) federated loRA fine-tuning,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Fed-SB: A silver bullet for extreme communication efficiency and performance in (private) federated loRA fine-tuning,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.558704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.322153Z digest=sha256:701e9f7b588bd800c88e59eb6e26fc6a9133b794348e7b6f59b413684e451b50

Observation ca1436d3-fede-474f-871c-c3a078db4535 · outbound

This paper cites A unified theory of decentralized sgd with changing topology and local updates,.

Decentralized Low-Rank Fine-Tuning of Large Language Models A unified theory of decentralized sgd with changing topology and local updates,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.541271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.327321Z digest=sha256:282c91120b84ee4e571906712f0b690e57766cba78b9b31a04c6b56020f5900c

Observation c7e54dfd-63b9-461c-b6bc-52a4aeda94c6 · outbound

This paper cites Fast linear iterations for distributed averaging,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Fast linear iterations for distributed averaging,

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.333326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.333326Z digest=sha256:939d9a34a2d393cc545b22d28633a05675dc75fe859943d136ecb1a0ac7b4510

Observation e1059f93-5c40-402c-9fef-701418cfd3a3 · outbound

This paper cites The largest eigenvalue of sparse random graphs,.

Decentralized Low-Rank Fine-Tuning of Large Language Models The largest eigenvalue of sparse random graphs,

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.502931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.339252Z digest=sha256:84c78db6e1511dc4a8deda31712cc35e9c76227c8ce5492bf36e8cdcf9510a35

Observation 4685cf91-546b-4ea9-8e18-652a7e44d268 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.487395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.345956Z digest=sha256:6153bfedf6a86d6bdf85340704d295c15930c344439e67bd48f7130b8a33d989

Observation 2512770e-bef6-42b0-b36d-b8fdbdcd335e · outbound

This paper cites The pascal recognising textual entailment challenge,.

Decentralized Low-Rank Fine-Tuning of Large Language Models The pascal recognising textual entailment challenge,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.472035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.352748Z digest=sha256:1a7d54482f5435463a7d6fc37d388376cbaee456189ae477337b28d4f4d42ed3

Observation a5e1dcbc-f299-4541-a413-c8937f745dec · outbound

This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

Decentralized Low-Rank Fine-Tuning of Large Language Models A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.358858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.358858Z digest=sha256:e636c67019a47dd918c7c0230f5ce34230b05b0a26c9ceb31d08da3c39d2a06e

Observation 1aa34f4e-95a2-4b36-9a49-2c0c4ab800a5 · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

Decentralized Low-Rank Fine-Tuning of Large Language Models Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.365699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.365699Z digest=sha256:963840b09cf0edd4100eea7d5f1c0c31d3711d1e4b11dff7d733a8bfe4e92c16

Observation 209e60ff-3e11-42b0-a8ae-7f5e9e63dc5d · outbound

This paper cites Fine-Tuning Language Models with Just Forward Passes.

Decentralized Low-Rank Fine-Tuning of Large Language Models Fine-Tuning Language Models with Just Forward Passes

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.372188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.372188Z digest=sha256:c4d7ab85f3644a4f10f1b7793ba55ab2154cab5ecaecb4447cc430783624d0d7

Observation df092da4-fabb-4c35-a61f-11c6a3ca7485 · outbound

This paper cites On the convergence of decentralized gradient descent,.

Decentralized Low-Rank Fine-Tuning of Large Language Models On the convergence of decentralized gradient descent,

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:48.378462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:48.378462Z digest=sha256:7a013c8a96f61c98c2fccafaa969f36961a15d05caae985b8cfae02c41b7c716

Observation 719af4c3-ef4f-4bf7-933c-f5e9137942c5 · outbound

This paper cites Decentralized gradient tracking with local steps,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Decentralized gradient tracking with local steps,

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.446581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.383586Z digest=sha256:b2c96e33de16e0b3918e872614efc3137f07074065aa3321920349b4357cff24

Observation 3e87e0f5-0538-4b0e-804b-e8b4e23faf4b · outbound

This paper cites Robust decentralized learning with local updates and gradient tracking,.

Decentralized Low-Rank Fine-Tuning of Large Language Models Robust decentralized learning with local updates and gradient tracking,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T14:26:49.430478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.388463Z digest=sha256:931d84fee66a2b8b1d52fecbab616f2c1a9c60f269f212260c17d83e94367d15

Observation 5835ae83-ef97-4365-b256-474bd4d40ae3 · outbound

This paper cites an unresolved cited work.

Decentralized Low-Rank Fine-Tuning of Large Language Models Unresolved cited work

Reference 85

Resolution
unresolved
raw_fallback, observed 2026-08-10T14:26:49.410512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-10T14:26:48.395288Z digest=sha256:2d125d4a40a1c4bc572a2a4bc81bced577f2b2e446bd9f83786b91c1ab3a1b03

Pith citing papers

Observation 3906c495-d8b2-43a7-8a2d-7ab7f898c474 · inbound

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models cites this paper.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Decentralized Low-Rank Fine-Tuning of Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T13:42:45.995786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:42:45.995786Z digest=sha256:4bfc37b986b8f6cbfb8237c8dbbfd7b4fa5e615eedc589b7e1ccafd7da073c3f

Observation f1c7de4f-cd57-4c93-99be-c63d5761959f · inbound

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning cites this paper.

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning Decentralized Low-Rank Fine-Tuning of Large Language Models

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:49:44.471794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-26T09:29:03.234211Z digest=sha256:d49166a16c9e4cae90391bd02dc0fded16bd2dd7f689cc63308f05390a73ab5c