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Paper Citation Record · LEDGER

Generative Flow Networks: Theory and Applications to Structure Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.05498.

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

pith.paper-citation-record.v1
2501.05498 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:56:41.359434Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:06:55.518875Z

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 2900ccd5-53d4-494c-b87e-2a36b2150404 · inbound

Revisiting Non-Acyclic GFlowNets in Discrete Environments cites this paper.

Revisiting Non-Acyclic GFlowNets in Discrete Environments Generative Flow Networks: Theory and Applications to Structure Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-08T11:56:41.359434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T11:56:41.359434Z digest=sha256:a56cd9dd3c3fb339d82659243c0413f58a3c302115208d0f12d079c5a6d95881

Observation b64c894a-e72f-435d-908a-c9850a8a5c8c · inbound

Your GFlowNet Secretly Learns an Optimal Transport Plan cites this paper.

Your GFlowNet Secretly Learns an Optimal Transport Plan Generative Flow Networks: Theory and Applications to Structure Learning

Reference 155

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:06:55.520243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T02:35:08.323804Z digest=sha256:b8b95b526765ba6c0f45c5ad36a0c7a0115ceea920624221964c63eca0cbdad6