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Paper Citation Record · LEDGER

AI capabilities can be significantly improved without expensive retraining

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:22:44.829284Z

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 31dc4491-51cd-4743-b9b5-81d2460fdd41 · inbound

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks cites this paper.

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks AI capabilities can be significantly improved without expensive retraining

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-08T15:22:44.829284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:22:44.829284Z digest=sha256:e65fa18e3460957ba7d0c1d87a72d978d9d9c4d0eeae30918d50c33c9f6f83bf

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.

source=arxiv_source observed=2026-08-06T17:52:50.240992Z digest=sha256:db62452e9181f1dd7a2383f6186004e8412b14c9a6f8a2ab35c8fdc3692ef960

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-01T08:11:23.860723Z digest=sha256:cf20d133534f6a90d83da871f45a26af207a150e8555a71aa249fc809760f5d0

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T00:12:56.745617Z digest=sha256:78fb761a8cbc70a864bd3455cd0f1f926f434085b43a2015e3b22112de232fdf

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.

source=pdf_text observed=2026-08-01T11:39:22.583471Z digest=sha256:aa626b497b21d093f7feb66f4908ac31643b4da0b99393a6513de4044215fd4e

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.

source=pdf_text observed=2026-08-04T01:39:47.687200Z digest=sha256:2ed7cfa17bc2f96d000b519b3937d8382c4e9a915ea18ad6ebc29f11c5259766