Paper Citation Record · LEDGER

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2106.04399.

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

pith.paper-citation-record.v1
2106.04399 v2

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 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:19:35.189618Z

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

27
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 cc1e7747-00b1-41c7-afd8-7013e307e433 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:12:31.295643Z

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.

Observation 3c38f39b-5531-41aa-aa07-2024313cb446 · inbound

Exploring Generative Networks for Manifolds with Non-Trivial Topology cites this paper.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T13:19:35.189618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5e75809-8fb8-4ca7-a574-ad4896ea483f · inbound

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage cites this paper.

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T14:20:02.633092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6171fbb-addc-4fe8-835b-f17f62f9b961 · inbound

Minimally dissipative multi-bit logical operations cites this paper.

Minimally dissipative multi-bit logical operations Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T21:39:19.073432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a496d5e-5412-4c1a-9424-cd97e020d32f · inbound

A Meta Reinforcement Learning Approach to Goals-Based Wealth Management cites this paper.

A Meta Reinforcement Learning Approach to Goals-Based Wealth Management Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 258

Resolution: verified exact; arxiv_id, observed 2026-05-08T18:44:01.652301Z

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.

Observation 39d05a94-a868-461e-9ab8-7beb1f87b4e6 · inbound

Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation cites this paper.

Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T16:55:50.488782Z

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.

Observation 95334be4-9206-4d81-80e0-168a0c438259 · inbound

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier cites this paper.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.198605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.