Paper Citation Record · LEDGER

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2110.02673.

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

pith.paper-citation-record.v1
2110.02673 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:19:35.169855Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T10:16:52.594622Z

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 5f2527be-1eb3-40da-8286-c4c0ed0c1cca · inbound

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

Exploring Generative Networks for Manifolds with Non-Trivial Topology Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T13:19:35.169855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77146e41-4190-43b6-a46f-9c463ffe8143 · inbound

Machine-learning approaches to accelerating lattice simulations cites this paper.

Machine-learning approaches to accelerating lattice simulations Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T11:38:41.583020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9c864b0-18dd-4aab-8dd5-5f2eb0190f57 · inbound

Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization cites this paper.

Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T04:55:54.379572Z

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 faf519f5-f925-4f86-97b5-c317de359204 · inbound

The critical slowing down in diffusion models cites this paper.

The critical slowing down in diffusion models Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:29.037841Z

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 58d4837f-f74b-47d2-888d-5962e480cc30 · inbound

The critical slowing down in diffusion models cites this paper.

The critical slowing down in diffusion models Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:54:02.763266Z

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 a71a67ac-7193-4077-aa35-8e78f5dc71f6 · inbound

SURF: Separation via Unsupervised Remixing Flow cites this paper.

SURF: Separation via Unsupervised Remixing Flow Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 207

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T10:16:52.596035Z

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.