Paper Citation Record · LEDGER

Using deep learning to construct stochastic local search SAT solvers with performance bounds

As of 6 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2309.11452.

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

pith.paper-citation-record.v1
2309.11452 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T06:23:10.906146Z

measured 35 of 35 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 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

35 of 35 outbound references displayed

verified exact1
verified fuzzy28
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 326610a2-f8d7-4594-ba60-50d1a2753fef · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.687555Z

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.

Observation 3b5e75af-c688-42af-ae01-27dbcb5a4bc7 · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.678898Z

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.

Observation e5917f69-ed57-4fe3-8f68-2d9d626c34b9 · outbound

This paper cites Machine learning methods in solving the boolean satisfiability problem.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Machine learning methods in solving the boolean satisfiability problem

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.658906Z

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.

Observation 7925d1ae-7766-43fc-b4f6-0e51b0b08561 · outbound

This paper cites Graph neural networks and boolean satisfiability.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Graph neural networks and boolean satisfiability

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.676106Z

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.

Observation 9d966ff1-ae17-429d-b399-8ed0dd27252f · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.696869Z

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.

Observation cac74c38-ad7b-4eb6-82f4-ef6050012639 · outbound

This paper cites Goal- aware neural SAT solver.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Goal- aware neural SAT solver

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.656006Z

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.

Observation 36f6744f-c9dc-40e1-802e-5fc99fa392c2 · outbound

This paper cites Guiding high-performance sat solvers with unsat-core predictions.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Guiding high-performance sat solvers with unsat-core predictions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.661680Z

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.

Observation 49e4a0eb-d8e8-4aac-9081-41046a305f75 · outbound

This paper cites Neural heuristics for sat solving.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Neural heuristics for sat solving

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.649535Z

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.

Observation f8037742-c609-4f3d-a949-541f12effb1c · outbound

This paper cites Enhancing sat solvers with glue variable predictions.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Enhancing sat solvers with glue variable predictions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.616584Z

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.

Observation 08ea3e36-423f-4829-95fc-16dbe2bb8d49 · outbound

This paper cites Learning local search heuristics for boolean satisfiability.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Learning local search heuristics for boolean satisfiability

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.652730Z

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.

Observation bcf00318-c2ae-4304-9370-d68c4dac14e4 · outbound

This paper cites NLocalSAT: Boosting local search with solution prediction.

Using deep learning to construct stochastic local search SAT solvers with performance bounds NLocalSAT: Boosting local search with solution prediction

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.592476Z

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.

Observation 8d9c9f7e-0e55-4ffa-b3a9-ac43bd9a016a · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.588966Z

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.

Observation 04920364-71f2-4d73-becd-9e43a440524e · outbound

This paper cites Moser and Gábor Tardos.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Moser and Gábor Tardos

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.646340Z

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.

Observation 510202b6-c381-4bb3-940c-d5b582eb7d2c · outbound

This paper cites Harris and Aravind Srinivasan.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Harris and Aravind Srinivasan

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.684732Z

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.

Observation 99c913c4-4e4d-4cb9-ab53-c30d19764910 · outbound

This paper cites Beyond the lovasz local lemma: Point to set correlations and their algorithmic applications.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Beyond the lovasz local lemma: Point to set correlations and their algorithmic applications

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.693655Z

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.

Observation 1d6b3f79-db29-4d15-911a-be6636a6d1ff · outbound

This paper cites Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, and Koray Kavukcuoglu

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.672393Z

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.

Observation 643e77a2-8d4d-414c-956f-a2391594e220 · outbound

This paper cites Solving mixed integer programs using neural networks.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Solving mixed integer programs using neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.667998Z

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.

Observation dfcc615c-0259-4825-8fbc-e77884b3ec3c · outbound

This paper cites Erd˝os and L.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Erd˝os and L

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.619662Z

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.

Observation 08e330e6-aa14-44f9-b1c3-0d2aede16826 · outbound

This paper cites An algorithmic proof of the lovasz local lemma via resampling oracles.

Using deep learning to construct stochastic local search SAT solvers with performance bounds An algorithmic proof of the lovasz local lemma via resampling oracles

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.682334Z

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.

Observation aefb0d4d-2e68-40f0-9667-b7358f15a35a · outbound

This paper cites Schöning.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Schöning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.690430Z

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.

Observation 635c6139-9640-4261-9bf9-8591b11a1b8a · outbound

This paper cites Papadimitriou.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Papadimitriou

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.622662Z

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.

Observation 5d661d67-01d1-475f-8131-62ad065e095b · outbound

This paper cites Selman, H.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Selman, H

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.612927Z

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.

Observation 2e59eb02-061b-4f45-a0c9-b821280e3c44 · outbound

This paper cites Choosing probability distributions for stochastic local search and the role of make versus break.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Choosing probability distributions for stochastic local search and the role of make versus break

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.636564Z

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.

Observation d37cb0cf-f81f-417f-9be2-2758b7442bc9 · outbound

This paper cites Battaglia, Jessica B.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Battaglia, Jessica B

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.639794Z

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.

Observation d2f4210c-6247-4904-bcec-0c20fc06baac · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.605884Z

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.

Observation 3c9e9bcf-f715-4e39-827e-eb17cfeaa577 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Using deep learning to construct stochastic local search SAT solvers with performance bounds Deep Learning using Rectified Linear Units (ReLU)

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-24T06:23:59.887532Z

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.

Observation 47e24c60-4185-409e-a599-48d31bfe40c7 · outbound

This paper cites an unresolved cited work.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:24:00.609445Z

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.

Observation 2cf45f99-9a06-4423-a48b-b931fe14a995 · outbound

This paper cites The Moser- Tardos Resample algorithm: Where is the limit? (an experimental inquiry), pages 159–171.

Using deep learning to construct stochastic local search SAT solvers with performance bounds The Moser- Tardos Resample algorithm: Where is the limit? (an experimental inquiry), pages 159–171

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.664780Z

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.

Observation bb6b0bde-95e9-4ea1-9ea0-a505f94dd3aa · outbound

This paper cites Critical behavior in the satisfiability of random boolean expressions.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Critical behavior in the satisfiability of random boolean expressions

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.643265Z

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.

Observation c0397118-47fd-461f-a080-5b8b7e929063 · outbound

This paper cites MassimoLauria/cnfgen: CNFgen registered with Zenodo, November 2019.

Using deep learning to construct stochastic local search SAT solvers with performance bounds MassimoLauria/cnfgen: CNFgen registered with Zenodo, November 2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.629580Z

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.

Observation 9b8b080d-d57a-453e-b0b1-5b7d270fd66f · outbound

This paper cites On the glucose sat solver.

Using deep learning to construct stochastic local search SAT solvers with performance bounds On the glucose sat solver

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.632949Z

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.

Observation 8727c219-e0e0-4262-971e-b51106d3fb55 · outbound

This paper cites PySAT: A Python toolkit for prototyping with SAT oracles.

Using deep learning to construct stochastic local search SAT solvers with performance bounds PySAT: A Python toolkit for prototyping with SAT oracles

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.626100Z

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.

Observation 278828f4-b829-4442-9d18-e540e8553efe · outbound

This paper cites Satenstein: Automatically building local search sat solvers from components.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Satenstein: Automatically building local search sat solvers from components

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.602764Z

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.

Observation 9c78b5bb-6a1b-4b37-9713-5765f7cb3318 · outbound

This paper cites JAX: composable transforma- tions of Python+NumPy programs.

Using deep learning to construct stochastic local search SAT solvers with performance bounds JAX: composable transforma- tions of Python+NumPy programs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.596046Z

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.

Observation 540628af-15a5-4f44-a4f4-f47cb7b60778 · outbound

This paper cites Jraph: A library for graph neural networks in jax.

Using deep learning to construct stochastic local search SAT solvers with performance bounds Jraph: A library for graph neural networks in jax

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:24:00.599266Z

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.

Pith citing papers

No inbound Pith citation observations are available.