Paper Citation Record · LEDGER

Generalized Group Data Attribution

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.09940.

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

pith.paper-citation-record.v1
2410.09940 v2

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 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:08:47.316990Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:08.335369Z

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 94046e50-e258-4b4c-a1cf-21f323b5c526 · inbound

MAGIC: Near-Optimal Data Attribution for Deep Learning cites this paper.

MAGIC: Near-Optimal Data Attribution for Deep Learning Generalized Group Data Attribution

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:08:47.316990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af4a7a52-e96a-4f52-921c-27d0b2322970 · inbound

DataMIL: Selecting Data for Robot Imitation Learning with Datamodels cites this paper.

DataMIL: Selecting Data for Robot Imitation Learning with Datamodels Generalized Group Data Attribution

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T21:34:17.201674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63f2d4b0-cecc-4865-8e92-f39cbabf2753 · inbound

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems cites this paper.

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems Generalized Group Data Attribution

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:17:48.330063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10963ca6-4187-4db3-bf05-9c3c3967d30f · inbound

Prototype Language Models cites this paper.

Prototype Language Models Generalized Group Data Attribution

Reference 173

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:17:08.337034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.