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Paper Citation Record · LEDGER

Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.23111.

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

pith.paper-citation-record.v1
2410.23111 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:35:03.831568Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:19:42.893014Z

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 d9b5e249-9d6f-47af-91cc-0f5eb366fd72 · inbound

Continual Gradient Low-Rank Projection Fine-Tuning for LLMs cites this paper.

Continual Gradient Low-Rank Projection Fine-Tuning for LLMs Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T20:35:03.831568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:35:03.831568Z digest=sha256:6b439df6a5c3684066c1cce5f6fb5b757decb61f50b11a919ff280e2be2a8614

Observation cb778588-41c1-42ce-b032-30d2cde1ac15 · inbound

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data cites this paper.

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T05:38:50.097225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:38:50.097225Z digest=sha256:084db1502d9bf5e39a58261bd797ba56d458b05d1da9cbe0947902c4d7330220

Observation 6a4ebc3d-d36d-41fa-b1ec-502992f717ee · inbound

Subspace-Constrained Federated Learning with Low-Rank Adaptation cites this paper.

Subspace-Constrained Federated Learning with Low-Rank Adaptation Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:19:42.894525Z

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=2026-06-26T10:18:24.570122Z digest=sha256:582381ff5722b88159b5c6ec84ab18ff601326f4607441f7db12f6fc1ad55322