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Paper Citation Record · LEDGER

Training Plug-n-Play Knowledge Modules with Deep Context Distillation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.08727.

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

pith.paper-citation-record.v1
2503.08727 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:26.753202Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:06:43.909755Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
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  • 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 e7804b4a-2cb1-4c0d-a04b-d20d792b3892 · inbound

Cartridges: Lightweight and general-purpose long context representations via self-study cites this paper.

Cartridges: Lightweight and general-purpose long context representations via self-study Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T06:04:26.753202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:04:26.753202Z digest=sha256:03afd1e507402d4234e89bc6377da4953b1f5ed80cbad2513a3759ccce814282

Observation 0f31cf42-e439-42ff-9dd2-223d17a32233 · 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 Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T05:55:56.486054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:55:56.486054Z digest=sha256:b9c8902f3df0eba648b363118ac05a32a7784af628d1a5e618927b6b3bc7093c

Observation ce89dc39-2daf-4851-ada4-258c916c460c · inbound

LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks cites this paper.

LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T19:34:39.829866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:34:39.829866Z digest=sha256:c6b5f9254456d494588eda00d0a41fa8cc3473365c5494237aa3c92187252cdc

Observation 8aa7a578-f093-4b4e-a31a-f794bf741385 · inbound

The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment cites this paper.

The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T00:15:56.160303Z

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.

source=pdf_text observed=2026-05-10T18:36:44.401045Z digest=sha256:95ff6b930bba10fbb4a40f6a407293c583ea67136ac24d72c58e58115d8b7f1c

Observation 83051b19-7be0-4b3c-87ba-ac48dc5ea0e0 · inbound

Nectar: Neural Estimation of Cached-Token Attention via Regression cites this paper.

Nectar: Neural Estimation of Cached-Token Attention via Regression Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:31:25.411661Z

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.

source=arxiv_source observed=2026-05-12T02:42:44.058913Z digest=sha256:a72a377c4e1fa713fd08e344431630a319c4a2178f36d98bddb93853aa56b7ce

Observation fa5103c1-7c81-41f1-b9d8-1221bc3b70e6 · inbound

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference cites this paper.

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T21:43:59.530206Z

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.

source=pdf_text observed=2026-06-29T21:37:51.638904Z digest=sha256:e330a8402915eec0c5ab132c667a2b94bbb5bc1c5bc286856f51049e5759497c

Observation 2d963b23-7fb6-4510-9e4c-1d35b08463b1 · inbound

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories cites this paper.

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 109

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T02:26:26.848806Z

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.

source=pdf_text observed=2026-06-28T10:56:13.058872Z digest=sha256:71e7a4461c9351308d89fdb19af1a2b8b140d289db0c6105c8ab94d857a1a731

Observation 88bd2993-8c41-40b8-b2c2-9be698bb00b0 · inbound

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories cites this paper.

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 109

Resolution
unresolved
no resolver link, observed 2026-07-13T07:44:25.325808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T07:44:25.325808Z digest=sha256:51762b885467d652e37676b3faf5adb0a573b91ac5233c1bac5f580ca7e2430f

Observation 525b26fc-588e-4f03-9495-6b899802d530 · inbound

Frames2LoRA: Parametric Video Internalization for Vision-Language Models cites this paper.

Frames2LoRA: Parametric Video Internalization for Vision-Language Models Training Plug-n-Play Knowledge Modules with Deep Context Distillation

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:06:43.911176Z

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.

source=arxiv_source observed=2026-06-28T07:13:05.791836Z digest=sha256:9257365a6a4ca14bc2354b1fa207c2392f72580fc84751448a74c341ed9771b5