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Paper Citation Record · LEDGER

A theory of continuous generative flow networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.12594.

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

pith.paper-citation-record.v1
2301.12594 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:09:54.666658Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T20:54:05.440469Z

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 7e07002a-76f2-4c2a-bd67-f1a2a7bd2045 · inbound

Effective Reward Specification in Deep Reinforcement Learning cites this paper.

Effective Reward Specification in Deep Reinforcement Learning A theory of continuous generative flow networks

Reference 178

Resolution
unresolved
no resolver link, observed 2026-08-11T19:09:54.666658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:09:54.666658Z digest=sha256:a8555a24e56c09d9db6c72da2535ea00917395614fa234b4a344a6965b715c90

Observation 3288098d-ae64-49db-90d5-6fcc3174f33a · inbound

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers cites this paper.

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers A theory of continuous generative flow networks

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-08T14:44:35.576159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:44:35.576159Z digest=sha256:751f0e8efe05b290c7fd633c190c5168e9dbc376e4eea360ab6ed068f357a703

Observation 066689af-5ab9-4982-98de-478814cb02ad · inbound

A Distributional Framework for Generative Modeling of Molecular Crystals cites this paper.

A Distributional Framework for Generative Modeling of Molecular Crystals A theory of continuous generative flow networks

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-07-07T20:54:05.442871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-07-07T20:44:04.685417Z digest=sha256:eb6b11c3b079aa9e13c0285d0c7ea29b30fdaed91b7203e0f8ea57c68e3cfe98