Paper Citation Record · LEDGER

Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2407.09499.

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

pith.paper-citation-record.v1
2407.09499 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:05:48.438242Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 13cdebbc-b775-41d1-bec0-a82790cd512f · inbound

How to Synthesize Text Data without Model Collapse? cites this paper.

How to Synthesize Text Data without Model Collapse? Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T12:05:48.438242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1534f07f-10d1-4c3a-83b4-fa9d758ee5a0 · inbound

Ambient Diffusion Omni: Training Good Models with Bad Data cites this paper.

Ambient Diffusion Omni: Training Good Models with Bad Data Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:13.676215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f96cbb0-982e-4f0c-ad1a-ca2d6b0d0ac5 · inbound

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs cites this paper.

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:59:51.732050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cbb0070-04db-4858-aea1-80b079085422 · inbound

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula cites this paper.

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T02:43:33.964748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb04ee4f-4a05-4033-b3f4-36aae757d151 · inbound

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences cites this paper.

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:54.464430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65aceb4b-875e-479d-86b4-1254db30f4e0 · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 127

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:57:21.516147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55ab5707-c49c-4d89-8ce6-e31d62478f06 · inbound

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs cites this paper.

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences

Reference 155

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T01:34:09.493852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.