Pith. sign in

Paper Citation Record · LEDGER

Offsite-Tuning: Transfer Learning without Full Model

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2302.04870.

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

pith.paper-citation-record.v1
2302.04870 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:28:50.717596Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T18:35:46.109639Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ec829da9-d0b4-4f07-9c87-9fde568fb2fd · inbound

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

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Offsite-Tuning: Transfer Learning without Full Model

Reference 251

Resolution
verified exact
arxiv_id, observed 2026-05-13T11:32:37.109803Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-13T11:32:36.738536Z digest=sha256:67e1deed46dfda980c4c7469f66cd5d3294173aae644372fb8b73efccfcba3dc

Observation a8717ad3-2f86-4d77-9c56-0d0ad8b65374 · inbound

CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment cites this paper.

CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Offsite-Tuning: Transfer Learning without Full Model

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-23T18:35:46.112811Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-23T18:35:10.313449Z digest=sha256:fdfcf63fc61a66dd9132f5235817aa49310463e10b49fb09c7981a78e950956b

Observation 33cd0778-1850-48a2-b679-6e61413a1f79 · inbound

ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression cites this paper.

ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression Offsite-Tuning: Transfer Learning without Full Model

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-11T16:46:03.501606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:46:03.501606Z digest=sha256:0191c12b8dabbc81686e5842bc999eec900fa806d7c0650e460a6b86599ddefa

Observation d81ac49d-6ff7-4a18-99dc-8d6e17a81b53 · inbound

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training cites this paper.

Label Privacy in Split Learning for Large Models with Parameter-Efficient Training Offsite-Tuning: Transfer Learning without Full Model

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T10:26:42.923827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T10:26:42.923827Z digest=sha256:5a236c907201d7f7df6b6bfa6a3ad58fda9e686a8c8da6e2ae4dee765d7c3555

Observation 1aa0d868-e8b5-42ef-9d8e-6dd1dba52c5c · inbound

Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions cites this paper.

Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions Offsite-Tuning: Transfer Learning without Full Model

Reference 154

Resolution
unresolved
no resolver link, observed 2026-08-16T12:28:50.717596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:28:50.717596Z digest=sha256:ce7eca32bfb6ecbe4493628b85b8651a1f67c4c6d03ede876e305fb11f843f48

Observation f40ceb06-56c4-4ea1-9e0d-097122db45b4 · inbound

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks cites this paper.

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks Offsite-Tuning: Transfer Learning without Full Model

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-16T10:44:46.261726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:44:46.261726Z digest=sha256:94b1b5ff347f66905b86ad9a871a0ee0c9c1af1501ad9de9b220532c9ae519e4

Observation b12c2171-8902-4417-8077-a83e47f51525 · inbound

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models cites this paper.

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models Offsite-Tuning: Transfer Learning without Full Model

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-16T05:31:27.284044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:31:27.284044Z digest=sha256:118cb1375fc935fafcb82fe188e06ad443a5cd3b7e4945c77456c17932652355

Observation f2f83a6e-0002-4dd5-8c95-f16df783b58a · inbound

FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning cites this paper.

FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning Offsite-Tuning: Transfer Learning without Full Model

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T23:15:59.614829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:15:59.614829Z digest=sha256:d5887f33232bd4fd3bab3a27ff98032fcc7a7d3df51e240157a4598c669ca0d9

Observation 8ea9242d-a19c-4c16-8760-22ac95ddc7bb · inbound

Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models cites this paper.

Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models Offsite-Tuning: Transfer Learning without Full Model

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T05:55:56.667840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:55:56.667840Z digest=sha256:ab1721f35a917151f0d1236b0a97819b55f779f5c7f07f4d862da6332eff3148

Observation 7261a1c6-85f6-413d-b75e-b6e44e88514d · inbound

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs cites this paper.

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs Offsite-Tuning: Transfer Learning without Full Model

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T23:51:21.660873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:51:21.660873Z digest=sha256:c2a5b0337568298e2468b3bbfcb491b080f6039e6b315663f004acc6fbd66454

Observation 4c5558ad-e363-4e58-a988-d8d55d92d8c2 · inbound

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models cites this paper.

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models Offsite-Tuning: Transfer Learning without Full Model

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T19:52:35.653920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:52:35.653920Z digest=sha256:022e08100c03070597b5ea48dad411cc3537423059278d7d1eaadcba7160e786

Observation b782e171-c48c-4618-aa8d-9b0db3d661f7 · inbound

Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library cites this paper.

Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library Offsite-Tuning: Transfer Learning without Full Model

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T15:59:59.428089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:59:59.428089Z digest=sha256:991f377a03fbb45aa8ffc861835e8311941c599799eb777b1cd33091da79e057

Observation 2dee7ccf-7f06-4855-88fc-314c27679a15 · inbound

ISACL: Internal State Analyzer for Copyrighted Training Data Leakage cites this paper.

ISACL: Internal State Analyzer for Copyrighted Training Data Leakage Offsite-Tuning: Transfer Learning without Full Model

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-05T16:50:27.555174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:50:27.555174Z digest=sha256:606850feb4dd3340dc435a954ea5423a1cd7b23042330a114bae82ca0fbb8774

Observation 46a3e9c3-b3d2-458a-9dd4-8727ec07a9ba · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion Offsite-Tuning: Transfer Learning without Full Model

Reference 51

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:56:05.966978Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-10T02:35:40.593397Z digest=sha256:685494781accb7c282f109e9e4540476eabe29102378e2f83a9480d2e4e6f172

Observation c0861dc2-94d1-4938-a675-52c702929913 · inbound

How Context Attribution Handles What the Model Already Knows cites this paper.

How Context Attribution Handles What the Model Already Knows Offsite-Tuning: Transfer Learning without Full Model

Reference 169

Resolution
unresolved
no resolver link, observed 2026-07-30T12:03:28.596693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-30T12:03:28.596693Z digest=sha256:718986cfc39b89f342d9036530feaa4e6a4c1f7488cfe89f8017703cc69b914b