Paper Citation Record · LEDGER

AI capabilities can be significantly improved without expensive retraining

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.07413.

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

pith.paper-citation-record.v1
2312.07413 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:52:50.240992Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:57.921808Z

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 ccf5aff4-cd22-4ee0-84a7-531d541564d2 · inbound

Compute Requirements for Algorithmic Innovation in Frontier AI Models cites this paper.

Compute Requirements for Algorithmic Innovation in Frontier AI Models AI capabilities can be significantly improved without expensive retraining

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T17:52:50.240992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c2edbcf-9e31-44fd-b347-8a9c7250d5d7 · inbound

Comprehensive AI governance requires addressing non-model gains cites this paper.

Comprehensive AI governance requires addressing non-model gains AI capabilities can be significantly improved without expensive retraining

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T08:15:31.624684Z

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.

Observation 4f4d2c9d-614d-49da-a1ba-5be9d7cc60a6 · inbound

Internal Data Repetition Destroys Language Models cites this paper.

Internal Data Repetition Destroys Language Models AI capabilities can be significantly improved without expensive retraining

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:49:57.923268Z

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.

Observation 29ede5a7-7cc9-411d-b37c-d36ed6f708c9 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals AI capabilities can be significantly improved without expensive retraining

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-01T11:39:22.583471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc59b98-1840-4561-9278-3d5203a95c98 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals AI capabilities can be significantly improved without expensive retraining

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T01:39:47.687200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.