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Paper Citation Record · LEDGER

Beyond neural scaling laws: beating power law scaling via data pruning

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2206.14486.

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

pith.paper-citation-record.v1
2206.14486 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:57:16.832443Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

85
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7511676a-e7ba-4e7a-9fe6-0e969504fa9e · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Beyond neural scaling laws: beating power law scaling via data pruning

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:35:21.231522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T01:35:21.150772Z digest=sha256:1ad29af2cbec541ad4c9334c9e587c4668d66567aa3b45981706ddfd01cc8113

Observation 6bde63d1-72b1-4c45-a1b7-84e92eeeb8a9 · inbound

Nougat: Neural Optical Understanding for Academic Documents cites this paper.

Nougat: Neural Optical Understanding for Academic Documents Beyond neural scaling laws: beating power law scaling via data pruning

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:42:12.625831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-16T09:42:12.463309Z digest=sha256:5fcfea456ff886bb21c2cb1eb19569ff1d76e369f8f5c42977f3efb1e8204128

Observation d569c468-a9e7-4518-bf3b-527727ad2c85 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Beyond neural scaling laws: beating power law scaling via data pruning

Reference 300

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:12:31.083140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:fcd9d018584b9ce72369f552782507ced6d8ba99f41ef322e69ac937324ea0dd

Observation b78c47d7-9c85-496f-9868-29d2c61bbca8 · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Beyond neural scaling laws: beating power law scaling via data pruning

Reference 160

Resolution
verified exact
arxiv_id, observed 2026-05-16T17:56:23.553283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-16T17:56:23.281678Z digest=sha256:76259ecb97b28e23e6a5e46e6d0318fef3f55668e6a9bb3eaa53e476246c62b0

Observation ec694083-e4a9-4592-a4ae-6a6cb9ce8094 · inbound

Foundation Models for Discovery and Exploration in Chemical Space cites this paper.

Foundation Models for Discovery and Exploration in Chemical Space Beyond neural scaling laws: beating power law scaling via data pruning

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-18T05:52:24.978352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T05:52:10.848118Z digest=sha256:bf48fb3f1f230ee7b7010fc10d27247f2fb6e164cdc2dea5f71a079531e4f06a

Observation efb598e6-9365-49f9-a162-c16faf6afc93 · inbound

Epistemic diversity across language models mitigates knowledge collapse cites this paper.

Epistemic diversity across language models mitigates knowledge collapse Beyond neural scaling laws: beating power law scaling via data pruning

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-03T15:57:16.832443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:57:16.832443Z digest=sha256:cfdeeb14cf4cca16c717109deddf86e1d35dcab6c4bc1cb2a7e372f952967b06

Observation fbc304b8-c642-4630-8fe1-8c32cdb27d01 · inbound

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency cites this paper.

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency Beyond neural scaling laws: beating power law scaling via data pruning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T11:25:09.568218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:25:09.568218Z digest=sha256:752cd68ba0b7370844752e13ef41ee1d394edf797b927c8cd63af3ce11fea03a

Observation 9c6cd556-ae92-42eb-9800-a198908f5360 · inbound

Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation cites this paper.

Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation Beyond neural scaling laws: beating power law scaling via data pruning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-03T10:49:15.488815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:49:15.488815Z digest=sha256:f036fcd85bdaba870a51b64fd57bf01a3888dd6ed1d9810f2da85b076080f527