Paper Citation Record · LEDGER

The Yin-Yang dataset

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:45:09.325911Z

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 32b3dcee-29c5-4185-879d-4d0fee80d79d · inbound

Event-based backpropagation on the neuromorphic platform SpiNNaker2 cites this paper.

Event-based backpropagation on the neuromorphic platform SpiNNaker2 The Yin-Yang dataset

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T11:45:09.325911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-23T06:30:58.430688+00:00.

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 2026-08-23T06:30:58.430688+00:00.

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.