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Paper Citation Record · LEDGER

ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.16187.

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

pith.paper-citation-record.v1
2403.16187 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:09:14.299953Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:06:34.597063Z

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 eebf33fb-65d8-42ba-a3b5-b570debd78e9 · inbound

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts cites this paper.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:14.299953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:14.299953Z digest=sha256:7ff09747b4ad6c6988f3fee76ee6673e86e174ceda82c7f9bbc54efef1a19ab7

Observation 2012c16e-8b9d-42ce-96eb-483e96aa9b3e · inbound

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts cites this paper.

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-22T13:06:34.599816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T13:06:03.463520Z digest=sha256:1f1036783d8200c5a2b2ee988fa32eff4989471f32e4dab304a8f304eb036de9

Observation 6c6d9dff-99d6-44a8-ba62-ca1a2aaf9010 · inbound

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints cites this paper.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:52.618341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:48:52.618341Z digest=sha256:6dba3318fa5ab5746bd714ba6c96e8ba77750303934857db4f5a202b7423145a

Observation a26cd787-9830-44fe-b122-55eefd1ccc8d · inbound

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation cites this paper.

GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T17:33:15.257502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:33:15.257502Z digest=sha256:72c0023dbbe19b4ad0ef47648868b94068a2ffd017c4a854ff1f741bd463a715

Observation 84a22cf1-045f-48d0-bd26-3af3484e790e · inbound

QR-LoRA: QR-Based Low-Rank Adaptation for Efficient Fine-Tuning of Large Language Models cites this paper.

QR-LoRA: QR-Based Low-Rank Adaptation for Efficient Fine-Tuning of Large Language Models ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T14:01:15.260967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:01:15.260967Z digest=sha256:86f07aea53018efbfeec487a6ff3594bedec5003f530ab8d2adef5f4077338e5

Observation 8f502975-d971-429f-be4c-281340f7a756 · inbound

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models cites this paper.

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T19:43:37.353951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:43:37.353951Z digest=sha256:690e802ced51dfeed01d6772cd92af6c895f3a0336e857a378db1f366f2f95b2