Paper Citation Record · LEDGER

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity

As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2506.03337.

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

pith.paper-citation-record.v1
2506.03337 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:16:23.379721Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:12:12.064235Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T08:48:01.010080Z

Reference resolution

44 of 44 outbound references displayed

verified exact2
verified fuzzy13
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fdb4c744-4f00-4fae-abaa-8e92880ae167 · outbound

This paper cites an unresolved cited work.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:19.749113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0f3bf3d-0882-4b8c-a40d-bcd12b99ecdb · outbound

This paper cites Federated Learning Based on Dynamic Regularization.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Federated Learning Based on Dynamic Regularization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:19.831388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf45c2db-c37a-47ef-bb49-0c7a2350fd75 · outbound

This paper cites Optimization Methods for Large-Scale Machine Learning.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Optimization Methods for Large-Scale Machine Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:19.941903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b94b8ca7-58fe-44a9-8d33-785666b5329a · outbound

This paper cites Languagemodels are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Languagemodels are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.034105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a3dd913-5ea5-429b-a167-62519923b2f6 · outbound

This paper cites Fine-grained theoretical analysis of federated zeroth-order optimization.Advances in Neural Information Processing Systems, 36, 2024.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Fine-grained theoretical analysis of federated zeroth-order optimization.Advances in Neural Information Processing Systems, 36, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:27.080591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c72ef9fd-1a01-4ef4-84bc-4321e8d03df6 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.207736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf7bf74c-f27a-4e11-9765-b2f53d81d56d · outbound

This paper cites The Llama 3 Herd of Models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity The Llama 3 Herd of Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.272607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3f21eaa-b62e-47b6-931e-a1279517c1e7 · outbound

This paper cites Communication-efficient stochastic zeroth-order optimization for federated learning.IEEE Transactions on Signal Processing, 70:5058–5073, 2022.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Communication-efficient stochastic zeroth-order optimization for federated learning.IEEE Transactions on Signal Processing, 70:5058–5073, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:26.937303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f169995d-d57c-4451-bb56-f256717cd7a8 · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.416022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beeb33f7-77e1-4298-a91a-79e4c1d01dec · outbound

This paper cites Pruning Large Language Models with Semi-Structural Adaptive Sparse Training.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:16:24.436227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27768946-2fd8-41cb-9bd1-b05064344234 · outbound

This paper cites Reddi, Sebastian U.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Reddi, Sebastian U

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:26.768995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa169567-cbe5-40dc-a2cd-f8a852830efd · outbound

This paper cites The winograd schema challenge.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity The winograd schema challenge

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.686847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d84fb4a-c071-4788-ae19-6cf762a36e35 · outbound

This paper cites Federated Learning on Non-IID Data Silos: An Experimental Study.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Federated Learning on Non-IID Data Silos: An Experimental Study

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.795118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1825b4f4-56e6-4597-af4d-ef34599f65a7 · outbound

This paper cites Federated Optimization in Heterogeneous Networks.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Federated Optimization in Heterogeneous Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.859882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5508f8c-5a14-4e94-aed4-369ef51b8e41 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity On the Convergence of FedAvg on Non-IID Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.939212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbba13a1-ff0a-480d-8cde-c5492d8a8492 · outbound

This paper cites Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:16:24.165987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1c471dd2-bf25-425d-aaff-1c277b839ead · outbound

This paper cites On the convergence of zeroth-order federated tuning for large language models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity On the convergence of zeroth-order federated tuning for large language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:26.582590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cbc0f52-33b6-4d07-bbee-77b2a551c631 · outbound

This paper cites Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning, 2024.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:21.196321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e149a59-7893-45b4-b69b-7ec3d714f960 · outbound

This paper cites Scissorhands: Exploiting the persistence of importance hypothesis for LLM KV cache compression at test time.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Scissorhands: Exploiting the persistence of importance hypothesis for LLM KV cache compression at test time

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:26.429789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d8b6117-a07f-47c1-acf9-9ace8c14d5ba · outbound

This paper cites Deja vu: Contextual sparsity for efficient llms at inference time.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Deja vu: Contextual sparsity for efficient llms at inference time

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:26.180492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a1e1a05-4941-4126-8c7e-e818c71566b1 · outbound

This paper cites SPP: Sparsity-preserved parameter-efficient fine-tuning for large language models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity SPP: Sparsity-preserved parameter-efficient fine-tuning for large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:25.997769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21995090-b69d-4105-ac2a-d01798f70c7e · outbound

This paper cites Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Fine-tuning language models with just forward passes.Advances in Neural Information Processing Systems, 36:53038–53075, 2023

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32eb43a7-ea7b-45c6-bba3-c375b973e081 · outbound

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

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Communication-efficient learning of deep networks from decentralized data

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:25.782947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc09178b-c527-442b-920e-b8d254987796 · outbound

This paper cites Local learning matters: Rethinking data heterogeneity in federated learning.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Local learning matters: Rethinking data heterogeneity in federated learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:21.665441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae2ce05-9d3e-4f1f-9580-82bfacc80c1c · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:21.769514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e3bd07f-b15e-4f41-9630-7a3e341fb34a · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:21.849827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9da1562d-6f92-4542-abe6-ecfc80a6e32b · outbound

This paper cites One-shot sensitivity-aware mixed sparsity pruning for large language models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity One-shot sensitivity-aware mixed sparsity pruning for large language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:25.552043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 664f0862-f9b3-4c56-b673-f2c5faba6b9b · outbound

This paper cites Recursive deep models for semantic compositionality over a senti- ment treebank.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Recursive deep models for semantic compositionality over a senti- ment treebank

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:25.362296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02f30a04-9317-409d-9e59-87d44676bc60 · outbound

This paper cites In defense of structural sparse adapters for concurrent llm serving.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity In defense of structural sparse adapters for concurrent llm serving

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.078410Z

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Unavailable: canonical work link unavailable.

Observation ccf9532c-57d1-427c-9ff6-c6021dbbece6 · outbound

This paper cites FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.169338Z

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Unavailable: canonical work link unavailable.

Observation 9afa0859-ff63-4820-93bc-12ef388e9583 · outbound

This paper cites an unresolved cited work.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.271292Z

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Unavailable: canonical work link unavailable.

Observation daa39695-925f-4f07-b280-1fdaa68f274d · outbound

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

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.350214Z

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Unavailable: canonical work link unavailable.

Observation 29e08bd5-e933-446c-8ceb-080c29de42ed · outbound

This paper cites Vincent Poor.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Vincent Poor

Reference 33

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T11:16:23.790364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 467e8493-f8ec-4550-b95f-021b981257c9 · outbound

This paper cites Structured Pruning of Large Language Models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Structured Pruning of Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.539186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53dd072e-406a-482e-b01c-0ee56dd90230 · outbound

This paper cites Soft prompt recovers compressed llms, transferably.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Soft prompt recovers compressed llms, transferably

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:25.170043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 021ac5b4-a9b4-490a-a173-5d20ad8d3229 · outbound

This paper cites Fedfed: Feature distillation against data heterogeneity in federated learning.Advances in Neural Information Processing Systems, 36, 2024.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Fedfed: Feature distillation against data heterogeneity in federated learning.Advances in Neural Information Processing Systems, 36, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:24.940068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ccf140a-8a0e-4587-bea0-afa0d36b11f1 · outbound

This paper cites Desirable companion for vertical federated learning: New zeroth-order gradient based algorithm.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Desirable companion for vertical federated learning: New zeroth-order gradient based algorithm

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:16:24.744223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3937d818-489e-417f-ba42-08ed29534dd3 · outbound

This paper cites Character-level convolutional networks for text classification.Advances in neural information processing systems, 28, 2015.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Character-level convolutional networks for text classification.Advances in neural information processing systems, 28, 2015

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.866923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d1bafca-1759-4089-8835-1a31d9a7976b · outbound

This paper cites Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, and Tianlong Chen.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, and Tianlong Chen

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:22.962538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ca45ddc-6dd6-4a4b-aa4d-3f385f7fd442 · outbound

This paper cites Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:23.075668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc9dae28-6f94-4597-90eb-0f96c4b79c6a · outbound

This paper cites Federated Learning with Non-IID Data.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Federated Learning with Non-IID Data

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:23.173208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40562781-a13f-4706-ac95-8103b211d093 · outbound

This paper cites Learn To be Efficient: Build Structured Sparsity in Large Language Models.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Learn To be Efficient: Build Structured Sparsity in Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:23.297275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d4ac30f-5025-4671-8c50-3799667ecd85 · outbound

This paper cites Sirius: Contextual Sparsity with Correction for Efficient LLMs.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Sirius: Contextual Sparsity with Correction for Efficient LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:23.379721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd4cba08-1588-458d-8758-3fff890f09d6 · outbound

This paper cites SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.624757Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 52365066-34db-4900-b12d-44fe023fded1 · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:48:01.011965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 725f1724-5409-46c4-b804-e576c963f7af · inbound

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation cites this paper.

Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T16:12:12.064235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.