Paper Citation Record · LEDGER

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning

As of 22 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 1 inbound Pith citation observation for arXiv:2605.01046.

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

pith.paper-citation-record.v1
2605.01046 v3

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:08:25.933242Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact2
verified fuzzy2
unresolved1
parse uncertain0
malformed identifier2
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f408bd4-4950-4b3d-833f-b9fe20298af2 · outbound

This paper cites Ji, Y ., Saratchandran, H., Gordon, C., Zhang, Z., and Lucey, S.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Ji, Y ., Saratchandran, H., Gordon, C., Zhang, Z., and Lucey, S

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T15:12:38.499956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9d1516a2-3bc1-4452-8c57-8be6ec09cdf1 · outbound

This paper cites Krause, J., Stark, M., Deng, J., and Fei-Fei, L.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Krause, J., Stark, M., Deng, J., and Fei-Fei, L

Reference 2

Resolution: malformed identifier; raw_fallback, observed 2026-07-06T15:12:38.498184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea1b20dd-7175-4d7c-8480-731359f26a50 · outbound

This paper cites Revisiting Natural Gradient for Deep Networks.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Revisiting Natural Gradient for Deep Networks

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T07:35:28.310911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a572646b-44fa-490b-8557-50de0492eff0 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Gemma: Open Models Based on Gemini Research and Technology

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T07:35:28.883207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4913dd3b-be64-480f-bc70-d0d8b06d0f61 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T07:35:28.313373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0439ef3c-c2c1-4b16-80b9-7418c67a629f · outbound

This paper cites ACM Trans.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning ACM Trans

Reference 6

Resolution: verified exact; doi, observed 2026-07-01T07:35:28.315216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bbf38db9-ce4d-4a20-9218-96599d22c780 · outbound

This paper cites ISBN 979-8-89176-251-0.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning ISBN 979-8-89176-251-0

Reference 7

Resolution: metadata mismatch; doi, observed 2026-07-01T07:35:28.317050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d63671b-3be8-42ed-8fd0-7f04732c28cc · outbound

This paper cites arXiv preprint arXiv:2410.01870 , year=.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning arXiv preprint arXiv:2410.01870 , year=

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:35:28.880814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8b577b3c-28b1-44f9-a4aa-5f92d75f6624 · outbound

This paper cites 15 Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning 15 Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T15:12:38.503325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 002bfe02-2a4e-490b-834f-ff33aeea4ede · outbound

This paper cites an unresolved cited work.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-07-06T15:12:38.501557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2e14037-079b-41e6-ac8d-71b7c090a372 · outbound

This paper cites For example, if W is a projection layer with m= 4096 and n= 4096 , then mn≈1.68×10 7, and F contains (mn)2 ≈2.8×10 14 entries.

Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning For example, if W is a projection layer with m= 4096 and n= 4096 , then mn≈1.68×10 7, and F contains (mn)2 ≈2.8×10 14 entries

Reference 11

Resolution: malformed identifier; raw_fallback, observed 2026-07-06T15:12:38.505289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 6462b643-34b3-4ec0-83ed-d8dea7331b61 · inbound

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection cites this paper.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T06:08:25.933242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.