Paper Citation Record · LEDGER

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2412.07972.

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

pith.paper-citation-record.v1
2412.07972 v4

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:29:41.220560Z

measured 19 of 19 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy3
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1672933e-c56f-486f-8eee-fdd65ccf74ec · outbound

This paper cites an unresolved cited work.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:29:41.536873Z

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 a2783b6e-88ee-4394-b365-d6d4ca656114 · outbound

This paper cites doi: 10.1088/1742-5468/acf8ba.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models doi: 10.1088/1742-5468/acf8ba

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.902495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58d2d4c0-5cbe-41b1-af55-0accb5d0cb39 · outbound

This paper cites Dynamical Regimes of Diffusion Models.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Dynamical Regimes of Diffusion Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.908792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad806647-f19a-4c67-bab7-04e23c15585c · outbound

This paper cites The probability flow ODE is provably fast.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models The probability flow ODE is provably fast

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.914246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 538bc8b5-e609-4dd5-a91d-363a39e1f9ff · outbound

This paper cites Analysis of learning a flow-based generative model from limited sample complexity.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Analysis of learning a flow-based generative model from limited sample complexity

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.920453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82cd1320-5bbd-4faf-bc0d-6c9424c4ee0e · outbound

This paper cites Learning Mixtures of Gaussians Using Diffusion Models.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Learning Mixtures of Gaussians Using Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.925775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0b0e368-a102-4cc3-ac33-248431df2aeb · outbound

This paper cites Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.946373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fa6ef64-d058-4acd-aa96-8eedb1551351 · outbound

This paper cites Sampling, Diffusions, and Stochastic Localization.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Sampling, Diffusions, and Stochastic Localization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.985060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca380fe1-704e-4ab5-afae-4bc84c57eb06 · outbound

This paper cites Spontaneous Symmetry Breaking in Generative Diffusion Models.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Spontaneous Symmetry Breaking in Generative Diffusion Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:41.040956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42db066e-67bb-4090-b58d-0057473ccce9 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:41.073919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45611f0c-2f79-4fbe-b30a-c3c1001e08ad · outbound

This paper cites Generative Modeling by Estimating Gradients of the Data Distribution.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Generative Modeling by Estimating Gradients of the Data Distribution

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:41.080111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e8cfa5-9c2d-4d12-8ccf-e73680261daf · outbound

This paper cites D E XPERIMENTAL DETAILS The model used for the MNIST experiment consists of a U-Net architecture (Ronneberger et al.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models D E XPERIMENTAL DETAILS The model used for the MNIST experiment consists of a U-Net architecture (Ronneberger et al

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:29:41.521183Z

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 fb78f32c-0696-436a-89a0-b4ea7f76b2a9 · outbound

This paper cites Then with ηt(ν) = E[Z|mt = ν] we have ˙νt = νt t − ηt(νt) t 21 Proof.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Then with ηt(ν) = E[Z|mt = ν] we have ˙νt = νt t − ηt(νt) t 21 Proof

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:29:41.500714Z

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 9bc34c9c-6a74-41c5-85d4-990a7b3450a9 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Adam: A Method for Stochastic Optimization

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.936222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ba1777b-2573-4796-b24a-0679d2af2b4a · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Denoising Diffusion Probabilistic Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.931462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43d205f8-b58f-43e1-a54f-99b12e95de81 · outbound

This paper cites 11 A P ROOF OF PROPOSITION 1 To prove Proposition 1, we will use the following three Lemmas that follow directly from Albergo et al.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models 11 A P ROOF OF PROPOSITION 1 To prove Proposition 1, we will use the following three Lemmas that follow directly from Albergo et al

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:29:41.551956Z

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 110789b7-e10e-4b33-ab64-28e122ce189f · outbound

This paper cites Critical windows: non-asymptotic theory for feature emergence in diffusion models.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Critical windows: non-asymptotic theory for feature emergence in diffusion models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.941207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a687acef-c485-4ae7-81fd-e3db7e300a9a · outbound

This paper cites The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.863451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e4f7c69-79df-428c-ac13-1c4ed4cdab88 · outbound

This paper cites Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T18:29:40.896799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.