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Paper Citation Record · LEDGER

Multi-objective Neural Architecture Search via Non-stationary Policy Gradient

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2001.08437.

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

pith.paper-citation-record.v1
2001.08437 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:42:05.887689Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:26:25.078747Z

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 e79af5e5-be45-40dc-8e9c-6f7d80f4ea35 · inbound

Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints cites this paper.

Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints Multi-objective Neural Architecture Search via Non-stationary Policy Gradient

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:25.081305Z

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=arxiv_source observed=2026-05-12T04:17:09.173852Z digest=sha256:e83af6bcc02834036414b0d2d4c0d2d324c8abbe0577673df94c989553808ad5

Observation 6b732a0b-c7af-4c53-9a43-5016e840e6ce · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts Multi-objective Neural Architecture Search via Non-stationary Policy Gradient

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-01T16:42:05.887689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T16:42:05.887689Z digest=sha256:94922e40c05aafc4fe5999971436fb7337d20c73374fd03b048c9b5878f68bbf