Paper Citation Record · LEDGER

Selecting Large Language Model to Fine-tune via Rectified Scaling Law

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.02314.

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

pith.paper-citation-record.v1
2402.02314 v3

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 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:57:25.551926Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:28:33.791543Z

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 98c0a0e9-293a-48ac-92f2-bdbd1ac37859 · inbound

Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies cites this paper.

Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-23T08:22:44.283247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7a409e31-a657-4b71-952c-3674a9eadcad · inbound

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework cites this paper.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.551926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e7a544f-7197-4a9a-8073-ec4958a08e87 · inbound

TFG-Flow: Training-free Guidance in Multimodal Generative Flow cites this paper.

TFG-Flow: Training-free Guidance in Multimodal Generative Flow Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T15:20:40.242143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b077f7b-fb24-485c-a199-e9f957af083f · inbound

Mordal: Automated Pretrained Model Selection for Vision Language Models cites this paper.

Mordal: Automated Pretrained Model Selection for Vision Language Models Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:46:19.922978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3886276-95a3-4a87-adc5-cf542bccd42b · inbound

Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance cites this paper.

Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:07.398833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba98ee34-d184-440b-bad3-3eb246adb16b · inbound

When Does Generating More Help? Disentangling Fixed-Source Synthesis from Source Expansion in Synthetic Data Scaling cites this paper.

When Does Generating More Help? Disentangling Fixed-Source Synthesis from Source Expansion in Synthetic Data Scaling Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T15:28:33.792908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.