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Paper Citation Record · LEDGER

The Yin-Yang dataset

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2102.08211.

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

pith.paper-citation-record.v1
2102.08211 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 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T03:18:58.883951Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T23:06:37.332903Z

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 a685757f-11f3-479c-8c6f-eba39af1cfa4 · inbound

Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning cites this paper.

Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning The Yin-Yang dataset

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-04T14:59:55.528092Z

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.

source=pdf_text observed=2026-06-26T02:02:23.398340Z digest=sha256:946ee75b2df8e5545cbd4320023201b5b0bd24fdcdfa7d402a85ab330cf00988

Observation fe73dfc1-7d29-4423-9050-5b9a421066b7 · inbound

Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource cites this paper.

Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource The Yin-Yang dataset

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T23:06:37.334101Z

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.

source=pdf_text observed=2026-07-09T23:05:38.397373Z digest=sha256:460be3000c56d68a2deee81b1b609d171d87210d2d0529001cfc47c29161055c

Observation 43f1b1f4-f99f-4c43-b5a7-76d92c1f648b · inbound

Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks cites this paper.

Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks The Yin-Yang dataset

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-13T03:18:58.883951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T03:18:58.883951Z digest=sha256:1bd1f9c3deb7f0a51f59b813b6ecd82d07231045eefc04ee80d622059db997fd