Paper Citation Record · LEDGER

Generation Properties of Stochastic Interpolation under Finite Training Set

As of 16 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2509.21925.

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

pith.paper-citation-record.v1
2509.21925 v3

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:55:55.343091Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T21:10:19.571440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T21:11:16.930955Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy1
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fa8713f-28d6-4584-9181-45a57f8e882e · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

Generation Properties of Stochastic Interpolation under Finite Training Set Building Normalizing Flows with Stochastic Interpolants

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.270255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1862a1d2-4f1e-431b-b2cb-3603b6431faa · outbound

This paper cites On Memorization in Diffusion Models.

Generation Properties of Stochastic Interpolation under Finite Training Set On Memorization in Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.291238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fc105a6-8c3c-4b8d-8858-13ac2198e61b · outbound

This paper cites Conditional Stochastic Interpolation for Generative Learning.

Generation Properties of Stochastic Interpolation under Finite Training Set Conditional Stochastic Interpolation for Generative Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.295576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423b9e48-ee78-4515-bc79-e12055d95159 · outbound

This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

Generation Properties of Stochastic Interpolation under Finite Training Set Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.300263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d410a63-fb7f-4889-a258-27af64a12f99 · outbound

This paper cites Flow Matching for Generative Modeling.

Generation Properties of Stochastic Interpolation under Finite Training Set Flow Matching for Generative Modeling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.304014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 502208b9-6f7e-4bea-9d3d-6d1e613c7505 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generation Properties of Stochastic Interpolation under Finite Training Set Score-Based Generative Modeling through Stochastic Differential Equations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.311590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c912818-2551-40b8-8c2b-af9a5dc9c008 · outbound

This paper cites Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem.

Generation Properties of Stochastic Interpolation under Finite Training Set Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.315787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67264517-0892-43fb-9797-37244da24991 · outbound

This paper cites Do Generated Data Always Help Contrastive Learning?.

Generation Properties of Stochastic Interpolation under Finite Training Set Do Generated Data Always Help Contrastive Learning?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.319386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0721e146-c583-4833-b36b-88072e778c29 · outbound

This paper cites Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning.

Generation Properties of Stochastic Interpolation under Finite Training Set Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.323258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a34f2a0-7e76-400e-b213-afde95dc64ee · outbound

This paper cites On the Generalization of Diffusion Model.

Generation Properties of Stochastic Interpolation under Finite Training Set On the Generalization of Diffusion Model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.327233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b26171f-a831-4151-8697-9de8ecc6cd5c · outbound

This paper cites Diffusion probabilistic models generalize when they fail to memorize.

Generation Properties of Stochastic Interpolation under Finite Training Set Diffusion probabilistic models generalize when they fail to memorize

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:55:55.642368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2bc7815f-cc40-42f4-9c04-8f9162117bd5 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Generation Properties of Stochastic Interpolation under Finite Training Set Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.335164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 437dc5bd-357c-4621-9038-da1b62ba770d · outbound

This paper cites The Emergence of Reproducibility and Generalizability in Diffusion Models.

Generation Properties of Stochastic Interpolation under Finite Training Set The Emergence of Reproducibility and Generalizability in Diffusion Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.339054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35878c04-7645-4320-9f42-be1e50a84c57 · outbound

This paper cites an unresolved cited work.

Generation Properties of Stochastic Interpolation under Finite Training Set Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:55:55.628676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9cb74730-8c08-4bb5-bdff-6405175fb6c9 · outbound

This paper cites Large Language Diffusion Models.

Generation Properties of Stochastic Interpolation under Finite Training Set Large Language Diffusion Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.307683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf60b243-fa57-4e04-8bd1-df06241c7b5e · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Generation Properties of Stochastic Interpolation under Finite Training Set Improved Baselines with Momentum Contrastive Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.283317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28ec58c8-c8f2-4404-a570-f5ca0d69b543 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Generation Properties of Stochastic Interpolation under Finite Training Set Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.274946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12a627de-346a-4f56-b97c-8e72388fc6f0 · outbound

This paper cites On the closed-form of flow matching: Generalization does not arise from target stochasticity.arXiv preprint arXiv:2506.03719,.

Generation Properties of Stochastic Interpolation under Finite Training Set On the closed-form of flow matching: Generalization does not arise from target stochasticity.arXiv preprint arXiv:2506.03719,

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.279225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ccbe4a4-2be6-4ce4-ab32-c823014e770c · outbound

This paper cites Scaling Diffusion Language Models via Adaptation from Autoregressive Models.

Generation Properties of Stochastic Interpolation under Finite Training Set Scaling Diffusion Language Models via Adaptation from Autoregressive Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:55:55.287152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0d6ea406-dc5f-4eca-90ce-a636206cf4b1 · inbound

On The Hidden Biases of Flow Matching Samplers cites this paper.

On The Hidden Biases of Flow Matching Samplers Generation Properties of Stochastic Interpolation under Finite Training Set

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-09T02:06:06.437341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.