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Paper Citation Record · LEDGER

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.03136.

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

pith.paper-citation-record.v1
2406.03136 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:39:14.166379Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:11:05.145102Z

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 20ee9adc-18df-482c-818c-d553dd484eb8 · inbound

Universal Approximation of Visual Autoregressive Transformers cites this paper.

Universal Approximation of Visual Autoregressive Transformers Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T16:39:14.166379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:39:14.166379Z digest=sha256:e0bdc3aa2600e41c028464fba4d14c6330f25a0d813c834e1e9569edb69ee9ae

Observation 3c3ed1ad-f5e9-435b-a09b-13604fc7fe22 · inbound

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse cites this paper.

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:11:05.244812Z

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=2026-08-07T15:11:00.238993Z digest=sha256:4d81f18f1a79ba4edfdb586ab5dce6954074174d07b15df91beed3d2997c5bbd