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Paper Citation Record · LEDGER

MAGIC: Near-Optimal Data Attribution for Deep Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2504.16430.

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

pith.paper-citation-record.v1
2504.16430 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:54:42.424199Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:08.354831Z

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 4c7db193-a0bf-4e0e-85a7-89e3682e91f4 · inbound

Better Training Data Attribution via Better Inverse Hessian-Vector Products cites this paper.

Better Training Data Attribution via Better Inverse Hessian-Vector Products MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:42.424199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:42.424199Z digest=sha256:f859dce06829b8d0b4bc6af8a4e30773f12bd370dad432d7c09256fb69f3b51b

Observation 589b7f10-d0f3-4193-a0e8-645a5b32c470 · inbound

LLM generation novelty through the lens of semantic similarity cites this paper.

LLM generation novelty through the lens of semantic similarity MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-04T07:22:05.367970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:22:05.367970Z digest=sha256:7041b26414fb2f2a231025e6b7f528d600a1fd3a6ff1bd852ac80e5e99a45ae3

Observation cf6fc1fa-11cb-4836-a304-ed9e132e9fde · inbound

Efficient Estimation of Kernel Surrogate Models for Task Attribution cites this paper.

Efficient Estimation of Kernel Surrogate Models for Task Attribution MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T08:00:44.858296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T07:57:57.953637Z digest=sha256:88d46241ee50af2f180cd0a7f164e1b001cbfbd15480fe68830585e4ae66fee2

Observation ece4c010-1723-45db-b8eb-2aad9ad3324b · inbound

How to sketch a learning algorithm cites this paper.

How to sketch a learning algorithm MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T05:30:56.453850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T18:07:19.602053Z digest=sha256:cf9f3f33b51f7c4e2594c062b07ca1016f575738c7b3d4adc82008c03fb38d44

Observation 11c75c0c-7f29-4b56-afba-2bdccf6d5149 · inbound

How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines cites this paper.

How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:13:50.446204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T23:12:08.842805Z digest=sha256:ac9326035dcebc559dad161b045a6d3e923bd7d88ce97c1f381b12b897d720de

Observation 0877e611-a7e8-44be-a61b-f9f97bcfde6f · inbound

Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics cites this paper.

Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:56:47.951304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T06:26:22.502014Z digest=sha256:506ec512f420e68dd0efbf78d127058cb09f3de92b3fee5dc9a9bac5220edc1a

Observation 2ac5f64b-8551-47a0-9bdc-da75901720b0 · inbound

Small edits, large models: How Wikipedia advocacy shapes LLM values cites this paper.

Small edits, large models: How Wikipedia advocacy shapes LLM values MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T09:05:36.485562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-01T09:03:09.977131Z digest=sha256:fea4d13c46d80287f840f39a4291cb564d483976261212b7d4412d206b373e54

Observation 56ead02d-163d-4a52-9e1a-53f1a5415b7b · inbound

Small edits, large models: How Wikipedia advocacy shapes LLM values cites this paper.

Small edits, large models: How Wikipedia advocacy shapes LLM values MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-14T19:23:22.815333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T19:23:22.815333Z digest=sha256:81510ee3f6d2dfa8ac69cd1ebf83b403d04b46282c2df185b0692347d458ef72

Observation 24ec7b89-af0a-4242-aa7d-bde8c7fb848c · inbound

Prototype Language Models cites this paper.

Prototype Language Models MAGIC: Near-Optimal Data Attribution for Deep Learning

Reference 163

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T16:17:08.356434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-02T16:13:43.039645Z digest=sha256:73d94d3c098310b40f49e7264edf120d7f91f4e4f6548a138a1e915042714187