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Paper Citation Record · LEDGER

Training Data Attribution via Approximate Unrolled Differentiation

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.12186.

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

pith.paper-citation-record.v1
2405.12186 v2

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 2026-08-17T06:30:58.91139+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:08:46.903859Z

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

1
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 bd25474d-9c55-43d6-857d-3142b934ff2b · inbound

Capturing the Temporal Dependence of Training Data Influence cites this paper.

Capturing the Temporal Dependence of Training Data Influence Training Data Attribution via Approximate Unrolled Differentiation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T17:02:33.676883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T17:02:33.676883Z digest=sha256:061c9ae7942bcbeea0bfc00ca0c346b3be18b388eff75f18e400de5cc2ff695d

Observation 483c882a-f64f-4e7d-acae-91c590f45f6e · inbound

Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search cites this paper.

Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search Training Data Attribution via Approximate Unrolled Differentiation

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:07:30.437834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-23T04:06:23.521344Z digest=sha256:b98203bfd3e30e76c3c6d7df41aea1115f870c77d77bf2f612844f5b45493e18

Observation 77e482bb-db5d-46d4-b11c-e29867d8c0a4 · inbound

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

MAGIC: Near-Optimal Data Attribution for Deep Learning Training Data Attribution via Approximate Unrolled Differentiation

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-16T11:08:46.903859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:08:46.903859Z digest=sha256:cb2d6fe93b81b751854b13ad0f4b7bdd896640c06a00f6301fcd3b75754feaa1

Observation 80fc41d0-17ec-41ec-9748-d54835f4572c · inbound

Daunce: Data Attribution through Uncertainty Estimation cites this paper.

Daunce: Data Attribution through Uncertainty Estimation Training Data Attribution via Approximate Unrolled Differentiation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T12:56:14.438057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:56:14.438057Z digest=sha256:8bf988879751453fd852d616a0aee154143ec5caf992cc8fb1850d6ee833a540

Observation c1800acf-baf2-4dcb-9391-ed2922cc871b · inbound

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

Better Training Data Attribution via Better Inverse Hessian-Vector Products Training Data Attribution via Approximate Unrolled Differentiation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T15:54:42.418110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:54:42.418110Z digest=sha256:0c339bca26275e586b551014968be64e7a399dc6ced506d25980766e6ee2c711

Observation 801940c5-ffe8-44dd-a511-4229c3c8f6e9 · inbound

What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis cites this paper.

What Is The Performance Ceiling of My Classifier? Utilizing Category-Wise Influence Functions for Pareto Frontier Analysis Training Data Attribution via Approximate Unrolled Differentiation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-04T11:38:25.550434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:38:25.550434Z digest=sha256:d7b931ba710bf5ca0abb8fdfa06de801758031fc0378d975afd5a4e6dcfcd028

Observation 886f89e9-31ef-42c4-a247-ea2348707e6d · inbound

Variance Reduction for Expectations with Diffusion Teachers cites this paper.

Variance Reduction for Expectations with Diffusion Teachers Training Data Attribution via Approximate Unrolled Differentiation

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-21T04:53:57.944260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T04:51:57.800006Z digest=sha256:9802f210e7f34ecae2ec5619f11426636a2c958ee2dd558a6bae7053f1c88924

Observation 40846ee9-8ff3-4e2d-a160-2d81711675ff · inbound

Variance Reduction for Expectations with Diffusion Teachers cites this paper.

Variance Reduction for Expectations with Diffusion Teachers Training Data Attribution via Approximate Unrolled Differentiation

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:45:24.640090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-25T05:41:10.076206Z digest=sha256:98558a17a32aa091a5fae0cfd8cab25e72e01a494b8b042fd95614b84de57bcf

Observation ef3f4805-4227-4989-9803-2738cfe415b7 · inbound

Validity Threats for Foundation Model Research cites this paper.

Validity Threats for Foundation Model Research Training Data Attribution via Approximate Unrolled Differentiation

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:36:44.910724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-28T06:52:41.653304Z digest=sha256:544aedac9fdde0a23e520104988aa0e2d61aa5f0f0f1ba9e3915982484f43ed3

Observation 5701b543-758b-44e4-843f-6cc7046ba452 · inbound

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior cites this paper.

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior Training Data Attribution via Approximate Unrolled Differentiation

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-26T08:49:14.959164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-26T08:45:34.884703Z digest=sha256:03437100ec00c83c5c5103913fe9ff109b35b7e3be13c6ace56a5e98c9f7b2cb