Paper Citation Record · LEDGER

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1711.00123.

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

pith.paper-citation-record.v1
1711.00123 v3

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 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:11:41.707236Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T16:58:42.888445Z

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 b7b5e85c-adb1-458a-8118-08dee8b66146 · inbound

Locality-aware Surrogates for Gradient-based Black-box Optimization cites this paper.

Locality-aware Surrogates for Gradient-based Black-box Optimization Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T21:11:41.707236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9515d663-bfd7-4268-8dde-615448804f37 · inbound

Learning to Theorize the World from Observation cites this paper.

Learning to Theorize the World from Observation Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Reference 162

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:21:30.346386Z

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.

Observation 8796012f-8dcc-463b-a36e-7c1dd344f449 · inbound

Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states cites this paper.

Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Reference 54

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T16:58:42.890088Z

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.

Observation 80617a49-3380-4d6a-9888-392e2ba4ea10 · inbound

PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF cites this paper.

PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-06-30T08:04:29.024005Z

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.