Paper Citation Record · LEDGER

Training Neural Samplers with Reverse Diffusive KL Divergence

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2410.12456.

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

pith.paper-citation-record.v1
2410.12456 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:44:35.567148Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:08:58.073496Z

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 26ebb693-85cf-416b-8c99-f7497f3c59ef · 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 Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T14:44:35.567148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52a073a4-203d-4ff5-94a6-fb8b474d576b · inbound

Neural Flow Samplers with Shortcut Models cites this paper.

Neural Flow Samplers with Shortcut Models Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T13:11:58.670824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea27392d-844f-4fe3-9bb7-f023d79f67b2 · inbound

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold cites this paper.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T12:10:07.618238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536e5f22-2691-420d-9cc8-16a93816b43f · 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 Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:20:01.086255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cca44a-3edb-42c2-a480-b0d6a5594bb7 · inbound

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework cites this paper.

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T19:28:36.165145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f188ec8-f354-4a15-a97d-8bb4c2d72567 · inbound

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling cites this paper.

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:46:45.400878Z

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 d3077ff9-6652-4186-b5ad-eca6e29a6506 · inbound

On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:08.580791Z

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 d217d265-fb7f-4805-9fbc-50510221aef3 · inbound

Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation cites this paper.

Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:58.074990Z

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.