Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:09:34.877783Z

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 e6b16c28-37ad-4104-9068-84d1100d22a3 · inbound

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond cites this paper.

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T20:09:34.877783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43727f96-0470-4bd7-8a0f-9446b99e94c7 · inbound

Circuit Complexity Bounds for Visual Autoregressive Model cites this paper.

Circuit Complexity Bounds for Visual Autoregressive Model Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T21:42:46.252834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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 2026-08-13T06:32:02.005865+00:00.