Paper Citation Record · LEDGER

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning

As of 12 July 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2604.19254.

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

pith.paper-citation-record.v1
2604.19254 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T02:13:07.109453Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-12T06:30:05.999651+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy7
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4ca52cd-2630-4222-b436-4bb2af3797a7 · outbound

This paper cites Adaptersoup: Weight averaging to improve generalization of pretrained language models.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Adaptersoup: Weight averaging to improve generalization of pretrained language models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.591362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 25b64644-59db-4c01-ba8a-34fa89eb1a2f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Training Verifiers to Solve Math Word Problems

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-11T13:11:08.815692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation a2ee7f3d-8ee5-4bdb-a8c2-218357e2dcc2 · outbound

This paper cites Parameter-efficient fine-tuning for large models: A comprehensive survey.Trans.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Parameter-efficient fine-tuning for large models: A comprehensive survey.Trans

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.600428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 05b03e48-2bd5-4f7c-a811-3b82c74cf345 · outbound

This paper cites The multilingual amazon reviews corpus.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning The multilingual amazon reviews corpus

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.587749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation f6101f46-5542-48fc-b81e-fc1f7cd1d38f · outbound

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

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning The power of scale for parameter-efficient prompt tuning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.604034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation dbe131e7-d925-4285-b2af-fcdf32a96ccf · outbound

This paper cites MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:11:08.865754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 8928db50-7b79-4728-a2b4-b83e612418cd · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022a.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022a

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.596806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 9f0a80aa-3473-4d20-9ae1-82ff9ba0defc · outbound

This paper cites Twenty Newsgroups.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Twenty Newsgroups

Reference 8

Resolution: metadata mismatch; doi, observed 2026-05-10T02:17:13.790189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation e267ed7a-9a05-4614-8299-08f7405fc1e4 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T13:11:08.965048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation a1aa3c3f-c557-4751-b4d5-802dc30cf527 · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T02:17:13.788358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 9344db22-7959-4480-a96c-57df900fbc95 · outbound

This paper cites Tiny-attention adapter: Contexts are more important than the number of parameters.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning Tiny-attention adapter: Contexts are more important than the number of parameters

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.584142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Observation 46b69899-88bf-4998-bd87-e434f6d2f6db · outbound

This paper cites explicit shadow models.ShadowPEFT supports two centralized shadow initialization strategies.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning explicit shadow models.ShadowPEFT supports two centralized shadow initialization strategies

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T00:17:18.580735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-12T06:30:05.999651+00:00.

Pith citing papers

No inbound Pith citation observations are available.