Paper Citation Record · LEDGER

Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2103.06957.

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

pith.paper-citation-record.v1
2103.06957 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:57.500053Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:54.723901Z

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 c84d95d2-426c-40b8-872f-b169e92853f3 · inbound

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model cites this paper.

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T13:21:25.299208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6766226-8f8d-4e65-9e52-e38d8370dc02 · inbound

Machine Learning in the 2HDM2S model for Dark Matter cites this paper.

Machine Learning in the 2HDM2S model for Dark Matter Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:47.436963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e0ec02a-b6f6-43bc-976e-5525092e8012 · inbound

Local Conformal Predictions for Calibrated Surrogates cites this paper.

Local Conformal Predictions for Calibrated Surrogates Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Reference 249

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:58:54.725196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 758eb47f-0061-411e-a4e9-5e0f669fd732 · inbound

Generative Amplification with Surrogate Monte Carlo cites this paper.

Generative Amplification with Surrogate Monte Carlo Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Reference 237

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:57.500053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.