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Paper Citation Record · LEDGER

Asymptotically unbiased estimation of physical observables with neural samplers

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1910.13496.

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

pith.paper-citation-record.v1
1910.13496 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 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:02:49.484812Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T03:22:21.033656Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 a7618073-e8e5-4cdd-8d51-5ba9c380ea20 · inbound

Studying Effective String Theory using deep generative models cites this paper.

Studying Effective String Theory using deep generative models Asymptotically unbiased estimation of physical observables with neural samplers

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T15:02:49.484812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:02:49.484812Z digest=sha256:deb4f8737384225d078b42ae9bbeed3dfd8557d81703311f226f677c06c66a1d

Observation f78dd0e0-27c7-4f6b-8fa5-b56888cb95c4 · inbound

Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory cites this paper.

Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory Asymptotically unbiased estimation of physical observables with neural samplers

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-18T03:22:21.036704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-18T03:21:13.881357Z digest=sha256:7bb7f4e3a516f1581ca4d87da1059f23d1e0ba131936d1790e6fc2741354a17f

Observation f3addb20-98e4-46d7-a76a-ed81b02349ad · inbound

Sampling two-dimensional spin systems with transformers cites this paper.

Sampling two-dimensional spin systems with transformers Asymptotically unbiased estimation of physical observables with neural samplers

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:31:28.448044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-07T05:59:00.370864Z digest=sha256:b5a34ee49e29f523a93f1633392f6264115fd4ce25be57b56d424cf349d8cd3f