Paper Citation Record · LEDGER

Reinforcement learning for graph theory, Parallelizing Wagner's approach

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2509.01607.

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

pith.paper-citation-record.v1
2509.01607 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:25:56.778384Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact2
verified fuzzy11
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3909692-2597-47fb-8666-9a58c688f572 · outbound

This paper cites Constructions in combinatorics via neural networks.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Constructions in combinatorics via neural networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.728914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc876007-75a3-4c84-8453-e823ebe38bb2 · outbound

This paper cites Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Reinforcement learning for graph theory, I. Reimplementation of Wagner's approach

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.732575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33c28d1b-13e4-4bb3-98ed-46fd18941111 · outbound

This paper cites Variable neighborhood search for extremal vertices: The autographix-iii system,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Variable neighborhood search for extremal vertices: The autographix-iii system,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.971500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f84bbd52-1152-40f4-9f91-03f6496f9525 · outbound

This paper cites Automated conjectures on upper bounds for the largest laplacian eigenvalue of graphs,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Automated conjectures on upper bounds for the largest laplacian eigenvalue of graphs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.961945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 525cbc72-2d57-47ce-8126-170c53935611 · outbound

This paper cites A nordhaus-gaddum type problem for the normalized laplacian spectrum and graph cheeger constant,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach A nordhaus-gaddum type problem for the normalized laplacian spectrum and graph cheeger constant,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.952923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b6f7fced-8f3c-4fc6-9e20-7ff5335423b9 · outbound

This paper cites A survey of automated conjectures in spectral graph theory,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach A survey of automated conjectures in spectral graph theory,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.944007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5e505a5f-b07f-46b5-8a7a-e637aab4946c · outbound

This paper cites Artificial intelligence and machine learning generated conjectures with TxGraffiti.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Artificial intelligence and machine learning generated conjectures with TxGraffiti

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:25:56.857427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56f0ed85-78f1-4849-be95-c0bc30bc5788 · outbound

This paper cites Reinforcement learning for graph theory, II. Small Ramsey numbers.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Reinforcement learning for graph theory, II. Small Ramsey numbers

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:25:56.844085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1434cae6-47a4-438b-ab46-b4d3b2dd099e · outbound

This paper cites Graph6java: A researcher–friendly java framework for testing conjectures in chemical graph theory,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Graph6java: A researcher–friendly java framework for testing conjectures in chemical graph theory,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.935544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88426275-cd53-42f3-a9ec-023d6b0f7ca3 · outbound

This paper cites Tempestas ex machina: A review of machine learning methods for wavefront control,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Tempestas ex machina: A review of machine learning methods for wavefront control,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.927038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db0ad815-2438-4756-94a7-c89b71f23ed5 · outbound

This paper cites A tutorial on the cross-entropy method,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach A tutorial on the cross-entropy method,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.917899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation acda165e-d4f6-4261-a28e-445dd6da99a7 · outbound

This paper cites A simple decentralized cross-entropy method,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach A simple decentralized cross-entropy method,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.909040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 821f2573-e007-40a5-a84b-20cf38c03066 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Adam: A Method for Stochastic Optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.762986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1932ce59-bb62-474b-944c-f7d4c1f7e83e · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Reinforcement learning for graph theory, Parallelizing Wagner's approach Gaussian Error Linear Units (GELUs)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.765976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdb10dc0-466c-4b6a-a1e5-a1e68251c4c5 · outbound

This paper cites Annealing adaptive search, cross-entropy, and stochastic approximation in global optimization,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Annealing adaptive search, cross-entropy, and stochastic approximation in global optimization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.900623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 034e05ac-66d9-435a-8210-a453820be5f0 · outbound

This paper cites PatternBoost: Constructions in Mathematics with a Little Help from AI.

Reinforcement learning for graph theory, Parallelizing Wagner's approach PatternBoost: Constructions in Mathematics with a Little Help from AI

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.771059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d4da545-7dd0-441a-99a4-570bd9ad8e1d · outbound

This paper cites Small ramsey numbers,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Small ramsey numbers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.891504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a73fddac-5036-4c2e-8efb-428fbab3df17 · outbound

This paper cites Action space shaping in deep reinforcement learning,.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Action space shaping in deep reinforcement learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:25:56.883056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7fbc6af2-d0ce-41d2-8d59-58a1fbd5e6fe · outbound

This paper cites Population Based Training of Neural Networks.

Reinforcement learning for graph theory, Parallelizing Wagner's approach Population Based Training of Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T12:25:56.778384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.