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Paper Citation Record · LEDGER

Beating the random assignment on constraint satisfaction problems of bounded degree

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1505.03424.

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

pith.paper-citation-record.v1
1505.03424 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:01:57.178163Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

8
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4f1190fc-3583-4c69-9716-7c9c2942d7cb · inbound

A brief history of quantum vs classical computational advantage cites this paper.

A brief history of quantum vs classical computational advantage Beating the random assignment on constraint satisfaction problems of bounded degree

Reference 115

Resolution
unresolved
no resolver link, observed 2026-08-11T12:01:57.178163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:01:57.178163Z digest=sha256:4aedbc173c964f3db7766c7090695cdbcc7690820c2c6cde6c8b178b64342641

Observation 7d106b34-20ab-45b4-a404-e4645add315d · inbound

Quantum Approximate Optimisation Applied to Graph Similarity cites this paper.

Quantum Approximate Optimisation Applied to Graph Similarity Beating the random assignment on constraint satisfaction problems of bounded degree

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T05:41:13.487412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:41:13.487412Z digest=sha256:456d7277ed0cf8e4a4d9cada9a2fb62bb8013bc0cc10e7623f6cd9776f0a2fdb

Observation bb93cc31-9030-4478-8ef7-969e29b44bae · inbound

A comprehensive benchmark of an Ising machine on the Max-Cut problem cites this paper.

A comprehensive benchmark of an Ising machine on the Max-Cut problem Beating the random assignment on constraint satisfaction problems of bounded degree

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T12:07:50.620041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:07:50.620041Z digest=sha256:65c8de01e0454cbbdbadca25cd2b9cb3d9317c2965d5519ce9d5ace9402325d1

Observation 8d0bb04c-5068-4e4c-8041-5c23ee7198ca · inbound

Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning cites this paper.

Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning Beating the random assignment on constraint satisfaction problems of bounded degree

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-05-25T05:05:21.866669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-25T05:00:50.557828Z digest=sha256:5cb7b16c78f35c12c9622385cfb4b7658f000741601d0b357ebfe1cd8917104b

Observation 0a208f0f-7633-442a-8c85-d89201797251 · inbound

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model cites this paper.

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model Beating the random assignment on constraint satisfaction problems of bounded degree

Reference 101

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T12:17:03.874293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-07-10T12:07:13.584708Z digest=sha256:9ccc09f9f6c5aebdce0d60f7d35e16afb96eeb6180dfc52e344e69f3ea874a81