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Paper Citation Record · LEDGER

Scaling Sparse Fine-Tuning to 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:2401.16405.

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

pith.paper-citation-record.v1
2401.16405 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 2026-08-07T15:27:20.778872Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T17:22:25.160984Z

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 d0c8353a-8f25-443a-a6b7-48986cc73bb3 · inbound

DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer cites this paper.

DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer Scaling Sparse Fine-Tuning to Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T15:27:20.778872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:27:20.778872Z digest=sha256:63c60cb0410ba1a3a562561f42ec7572f64e504d435ca42764f53c1b8172b209

Observation d967acb1-ebca-428d-bb44-4eb0e7db472b · inbound

Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution cites this paper.

Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution Scaling Sparse Fine-Tuning to Large Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T12:41:42.470869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:41:42.470869Z digest=sha256:800ec6cecde758597a14dafde4f7994893d279c498e4ba4294b2dfaf2a890ebc

Observation 0a4aeed2-eebd-48aa-bb2f-dd851460e72e · inbound

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts cites this paper.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Scaling Sparse Fine-Tuning to Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T19:07:33.147050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:07:33.147050Z digest=sha256:e169042b036e65af25dc9e48f3b40758b3e570f45a8cf35278c16c0e332d5af3

Observation ddf44ea6-beb2-429c-9c30-f61b3ae30fb7 · inbound

One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning cites this paper.

One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning Scaling Sparse Fine-Tuning to Large Language Models

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:51:07.475875Z

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=2026-05-08T13:40:50.908566Z digest=sha256:79703e2019624ad69db6f7cacaee12e53b66fe609d5c28f1184baec5f99f6aeb

Observation 445481ba-1715-4662-9a6a-985ff5de22d4 · inbound

When Data Is Scarce: Scaling Sparse Language Models with Repeated Training cites this paper.

When Data Is Scarce: Scaling Sparse Language Models with Repeated Training Scaling Sparse Fine-Tuning to Large Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-28T17:22:25.162752Z

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=2026-06-28T17:16:48.536850Z digest=sha256:6331f3ca4d7fe250218ababe562470bb42980a5909f86ee6321d1e833b74f552

Observation ad14e144-d21f-4ae8-aec8-dae0ee02fcdb · inbound

Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning cites this paper.

Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Scaling Sparse Fine-Tuning to Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-13T04:08:39.594367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T04:08:39.594367Z digest=sha256:9f7d415c3409c19145e06ea63224ff0ff8baae4801c1335ed7c91917ba7775df