Paper Citation Record · LEDGER

Federated Co-tuning Framework for Large and Small Language Models

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 2 inbound Pith citation observations for arXiv:2411.11707.

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

pith.paper-citation-record.v1
2411.11707 v3

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T17:08:05.240432Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:48.127431Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-22T01:44:30.361385Z

Reference resolution

24 of 24 outbound references displayed

verified exact6
verified fuzzy8
unresolved1
parse uncertain1
malformed identifier0
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f6ed063-8342-4012-964c-4c33b3359b09 · outbound

This paper cites an unresolved cited work.

Federated Co-tuning Framework for Large and Small Language Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-23T17:08:12.770390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f8907a9-12af-4c8d-8a25-2ccb3ec352f1 · outbound

This paper cites Practical Secure Aggregation for Federated Learning on User-Held Data.

Federated Co-tuning Framework for Large and Small Language Models Practical Secure Aggregation for Federated Learning on User-Held Data

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.533439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 245ee8b5-da37-44c5-93b5-896ecec8852f · outbound

This paper cites FedAdapter: Efficient Federated Learning for Modern NLP.

Federated Co-tuning Framework for Large and Small Language Models FedAdapter: Efficient Federated Learning for Modern NLP

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.544845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf915a3a-eea2-46c6-b061-1d226721943c · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Federated Co-tuning Framework for Large and Small Language Models Advances in neural information processing systems 30 (2017)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.768165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 283aacc6-2d11-4ea9-b262-57288dad88e5 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Federated Co-tuning Framework for Large and Small Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.521557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98914498-f2b5-46cb-b313-8f208b0d12c0 · outbound

This paper cites https://doi.org/10.5281/zenodo.10256836,https://zenodo.

Federated Co-tuning Framework for Large and Small Language Models https://doi.org/10.5281/zenodo.10256836,https://zenodo

Reference 6

Resolution: verified exact; doi, observed 2026-05-23T17:08:12.193398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d61aa917-9690-4960-afc3-9cd5588976bc · outbound

This paper cites Interna- tional Journal of Computer Vision129(6), 1789–1819 (2021).

Federated Co-tuning Framework for Large and Small Language Models Interna- tional Journal of Computer Vision129(6), 1789–1819 (2021)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.754362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ae3a64d1-8bda-436c-b7cc-bcb0814204e1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Federated Co-tuning Framework for Large and Small Language Models Distilling the Knowledge in a Neural Network

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.514145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c250e42-980a-4c64-be69-6609cc28cfdb · outbound

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

Federated Co-tuning Framework for Large and Small Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.525301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27727469-0287-430b-86a8-6eb991781901 · outbound

This paper cites In: Artificial intelligence and statistics.

Federated Co-tuning Framework for Large and Small Language Models In: Artificial intelligence and statistics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.751895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ddb102f-61da-4def-a6c0-32542abe78ee · outbound

This paper cites In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Federated Co-tuning Framework for Large and Small Language Models In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.761424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 077014a2-32a0-4f35-9a24-340326676cd4 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Federated Co-tuning Framework for Large and Small Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.529428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c53ce63-52ed-4d7d-8d59-b1df5b184b27 · outbound

This paper cites an unresolved cited work.

Federated Co-tuning Framework for Large and Small Language Models Unresolved cited work

Reference 13

Resolution: parse uncertain; raw_fallback, observed 2026-05-23T17:08:12.765776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5730c4a-f8c2-4925-9d0f-b64b3982ef37 · outbound

This paper cites In: Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition.

Federated Co-tuning Framework for Large and Small Language Models In: Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.763646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6cdeb3e-b6da-46b6-9ea0-d6d53df5d016 · outbound

This paper cites OpenAI blog1(8), 9 (2019).

Federated Co-tuning Framework for Large and Small Language Models OpenAI blog1(8), 9 (2019)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.756630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 002b0761-0cb4-4ad5-9c48-b7e3d39d4427 · outbound

This paper cites Advances and Open Challenges in Federated Foundation Models.

Federated Co-tuning Framework for Large and Small Language Models Advances and Open Challenges in Federated Foundation Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.548524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fb522c3-3506-4cd6-88bd-3ea862fb6c9d · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Federated Co-tuning Framework for Large and Small Language Models CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.552644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddb9bf6d-3cb9-49e8-9f86-d858df050860 · outbound

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

Federated Co-tuning Framework for Large and Small Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.537550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7c5167f-bcb3-4a66-8813-b22b2502aead · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

Federated Co-tuning Framework for Large and Small Language Models Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.541241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5db131b2-82f9-4a28-87f0-6afee8ba4895 · outbound

This paper cites Synthesis Lectures on Artificial Intelligence and Machine Learning13(3), 1–207 (2019).

Federated Co-tuning Framework for Large and Small Language Models Synthesis Lectures on Artificial Intelligence and Machine Learning13(3), 1–207 (2019)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.759026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b19c740-c229-45dc-84e3-1e0ccc58d769 · outbound

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

Federated Co-tuning Framework for Large and Small Language Models OPT: Open Pre-trained Transformer Language Models

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T17:08:12.517620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48b9ef6e-6607-4022-bc05-1ee49ab40102 · outbound

This paper cites In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition.

Federated Co-tuning Framework for Large and Small Language Models In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T17:08:12.749732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b91d91db-3546-47af-8507-463a916f32b2 · outbound

This paper cites When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods.

Federated Co-tuning Framework for Large and Small Language Models When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.557061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0f4904d-fc5f-417f-b12d-3d087ae8d9b3 · outbound

This paper cites FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning.

Federated Co-tuning Framework for Large and Small Language Models FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:08:12.561324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation f5f267c6-7595-4ed3-a3a8-5b6504c225b4 · inbound

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model cites this paper.

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model Federated Co-tuning Framework for Large and Small Language Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-22T01:44:30.362825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fca98122-c0fe-4b9d-9f03-70aaa2c82326 · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Federated Co-tuning Framework for Large and Small Language Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.127431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.