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Paper Citation Record · LEDGER

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2006.02425.

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

pith.paper-citation-record.v1
2006.02425 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:33:45.020302Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:16:00.700568Z

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 4c0fb954-5707-4528-b79f-280ddc137ff2 · inbound

Learning Broken Symmetries with Approximate Invariance cites this paper.

Learning Broken Symmetries with Approximate Invariance Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T04:33:45.020302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T04:33:45.020302Z digest=sha256:a40fa1931e9a3385a5d7e26bff0cf6643d653a1e4c8db2cb9cb89ef8b9b4ec57

Observation 983a0aed-15e2-4fe3-b1e9-d07083c0fd41 · inbound

Simulating the Hubbard Model with Equivariant Normalizing Flows cites this paper.

Simulating the Hubbard Model with Equivariant Normalizing Flows Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T20:48:33.784857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:48:33.784857Z digest=sha256:aef190a7f5ad5ffc4a267ef78dd9d5d77cbb3bef924ebdfeb9ac58f9f3bb8905

Observation 603257de-7c45-4b80-b6a5-584b5c0cf9e5 · inbound

Scalable Inference-Time Annealing with Surrogate Likelihood Estimators cites this paper.

Scalable Inference-Time Annealing with Surrogate Likelihood Estimators Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:16:00.702436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-28T22:54:55.415927Z digest=sha256:076a5a95298781e3953410f310733375a9371cf35f648b3198cf26897f855813