Paper Citation Record · LEDGER

Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2302.06960.

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

pith.paper-citation-record.v1
2302.06960 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:40:15.552837Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:50:12.692359Z

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 31d18014-3ab3-4886-a212-382479d44f01 · inbound

Efficient Alignment of Large Language Models via Data Sampling cites this paper.

Efficient Alignment of Large Language Models via Data Sampling Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T19:40:15.552837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d012d466-3a5b-429d-8e26-14cf41ee41ce · inbound

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework cites this paper.

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:27.195165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ce6b77f-a939-4976-92dc-5cdf10ea3543 · inbound

Data Selection Through Iterative Self-Filtering for Vision-Language Settings cites this paper.

Data Selection Through Iterative Self-Filtering for Vision-Language Settings Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

Reference 191

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:44.953283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d03bb547-aec3-4cf0-bd30-0988199c3e06 · inbound

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity cites this paper.

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity Data pruning and neural scaling laws: fundamental limitations of score-based algorithms

Reference 212

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:50:12.694656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.