Paper Citation Record · LEDGER

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.20221.

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

pith.paper-citation-record.v1
2505.20221 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:05:32.930910Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

12 of 12 outbound references displayed

verified exact1
verified fuzzy2
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee29435d-542e-440e-9731-502ce5272c54 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Adam: A Method for Stochastic Optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:31.824218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04737efa-b80c-4a58-89a3-6a42f2684de6 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:32.246185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f228a7-9655-467b-89c1-12f52929a55f · outbound

This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:32.664999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23d3986e-249e-40c1-808f-bff583c22473 · outbound

This paper cites (2017) andWNNJang et al.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training (2017) andWNNJang et al

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:33.992867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 04c799ef-131f-4160-b4ee-d17c2d62e6c2 · outbound

This paper cites For reproducibility, we generate 50 trajectories per seed across 5 random seeds (0-4), where the first 30 trajectories use the 3-layer MLP and the remaining 20 use the 2-layer MLP.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training For reproducibility, we generate 50 trajectories per seed across 5 random seeds (0-4), where the first 30 trajectories use the 3-layer MLP and the remaining 20 use the 2-layer MLP

Reference 64

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:05:33.815752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9cedefdf-c42e-4e16-a4f0-c357404858cc · outbound

This paper cites Ian goodfellow, yoshua bengio, and aaron courville: Deep learning: The mit press, 2016, 800 pp, isbn: 0262035618.Genetic programming and evolvable machines, 19(1):305–307,.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Ian goodfellow, yoshua bengio, and aaron courville: Deep learning: The mit press, 2016, 800 pp, isbn: 0262035618.Genetic programming and evolvable machines, 19(1):305–307,

Reference 1989

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:05:34.257516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c3dc0bfd-0805-4dc7-8c6e-70156af08052 · outbound

This paper cites Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:32.408604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67183a16-652b-41bd-bece-a1b24ea2888d · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:31.709438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54dcfeed-9396-4bd8-898d-59c24ebd923c · outbound

This paper cites Flow Matching Guide and Code.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Flow Matching Guide and Code

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:32.759351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3fa61ab-4da0-42f6-89fc-c112513dc4f7 · outbound

This paper cites Introspection: Accelerating Neural Network Training By Learning Weight Evolution.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Introspection: Accelerating Neural Network Training By Learning Weight Evolution

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:05:33.390196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 225e93f8-dc6a-478c-a531-b1913a86d37a · outbound

This paper cites A survey of time series foundation models: Generalizing time series representation with large language mode.arXiv preprint arXiv:2405.02358,.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training A survey of time series foundation models: Generalizing time series representation with large language mode.arXiv preprint arXiv:2405.02358,

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:05:32.321065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76206885-e11a-4df8-b3e0-f21e22f0595a · outbound

This paper cites Less is More: Efficient Weight Farcasting with 1-Layer Neural Network.

Gradient Flow Matching for Learning Update Dynamics in Neural Network Training Less is More: Efficient Weight Farcasting with 1-Layer Neural Network

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:05:33.592851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.