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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 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 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:06:56.200632Z

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T01:35:21.150772Z digest=sha256:a3fd387f6b999d0600eff21c32b57b4d757cc3cbb7c00e84a775b1e82bee2f58

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T09:42:12.463309Z digest=sha256:c92546ee9e417a6b092082a563692c885d4eeaeb7eed267b89f4b4173a50afc4

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:128e86c32f9f63d3e9096623b7d65365fc4cfa8d730917951583a3e1492dfeb3

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T17:56:23.281678Z digest=sha256:5100946a8c2bee3225147ff732392ef6ed6cf169389493f011821887d32e0cd3

Observation c628f9f8-2046-4686-84b1-1a49bdccd5cd · inbound

X-Factor: Quality Is a Dataset-Intrinsic Property cites this paper.

X-Factor: Quality Is a Dataset-Intrinsic Property Beyond neural scaling laws: beating power law scaling via data pruning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T13:06:56.200632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:06:56.200632Z digest=sha256:a3876ebe8655eecd4d27aacf79c160bbb65e43d65902ec042904da1902948324

Observation 061e9dd0-0aba-4d73-9ffc-af81e7a6bfa7 · inbound

Essential-Web v1.0: 24T tokens of organized web data cites this paper.

Essential-Web v1.0: 24T tokens of organized web data Beyond neural scaling laws: beating power law scaling via data pruning

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T00:24:47.187123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:24:47.187123Z digest=sha256:aae4a67aad9d6832a7a8f98eb3eb308254079515373505b5c1ec8be60bdad4c8

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T05:52:10.848118Z digest=sha256:1edc88d0f08b0e8ff127e5985782f5da9073a094d75774097141fabe71ab481e

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:e78f7126cef05be6c72b488729774524b2f44aaade9af637f34b308f3435cb1b

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=sha256:1c6ff33958ac8a1659fdef8cbfa5f2d15479d7b4584445e5829c3bc49795789b

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:aa0d2819f147fbaf5e24be51a1937d3ff10a28ea30bc932f67295582c7e72ade