Paper Citation Record · LEDGER

Extracting Problem Structure with LLMs for Optimized SAT Local Search

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.14630.

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

pith.paper-citation-record.v1
2501.14630 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:00:54.897618Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

46 of 46 outbound references displayed

verified exact13
verified fuzzy14
unresolved18
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84cdb05b-524d-4bf9-b679-92c7bb9ad643 · outbound

This paper cites Efficient inference of optimal decision trees.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Efficient inference of optimal decision trees

Reference 1

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Observation 74ea0d19-c842-45e2-b53f-943cffda8979 · outbound

This paper cites Boosting the performance of SLS and CDCL solvers by preprocessor tuning.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Boosting the performance of SLS and CDCL solvers by preprocessor tuning

Reference 2

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Observation fb25ce43-aee8-499a-b398-106f6749ac1b · outbound

This paper cites Minimising decision tree size as combinatorial optimisation.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Minimising decision tree size as combinatorial optimisation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.947132Z

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Observation 741c9132-c3c5-4855-b21c-2acc3d68387e · outbound

This paper cites CaDiCaL , Kissat , Paracooba , Plingeling , and Treengeling entering the SAT competition 2020.

Extracting Problem Structure with LLMs for Optimized SAT Local Search CaDiCaL , Kissat , Paracooba , Plingeling , and Treengeling entering the SAT competition 2020

Reference 5

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Observation 73e0528f-c5b2-4235-9fda-8a3cd650f830 · outbound

This paper cites Cadical 2.0.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Cadical 2.0

Reference 6

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Source-reported events for the cited work

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Observation 5ae711dc-34cf-47ad-ae34-0542abecb15e · outbound

This paper cites Yet another local search solver and Lingeling and friends entering the SAT Competition 2014.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Yet another local search solver and Lingeling and friends entering the SAT Competition 2014

Reference 7

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Observation feb2742d-18ef-437d-8ae2-3b658a04c525 · outbound

This paper cites CaDiCaL at the SAT race 2019.

Extracting Problem Structure with LLMs for Optimized SAT Local Search CaDiCaL at the SAT race 2019

Reference 8

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Observation f1cf30a1-173c-40eb-8f21-2fd21d4e2998 · outbound

This paper cites New methods to color the vertices of a graph.

Extracting Problem Structure with LLMs for Optimized SAT Local Search New methods to color the vertices of a graph

Reference 9

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Observation 52344670-8347-4901-b050-692736499b16 · outbound

This paper cites Deep cooperation of CDCL and local search for SAT.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Deep cooperation of CDCL and local search for SAT

Reference 10

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.440752Z

Source-reported events for the cited work

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Observation fa415553-e974-4e21-9f79-e0e3910a5e07 · outbound

This paper cites Better decision heuristics in CDCL through local search and target phases.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Better decision heuristics in CDCL through local search and target phases

Reference 11

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.422199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4d276cd7-9f83-4b86-b331-c0fc9db78d7e · outbound

This paper cites Large Language Models for Compiler Optimization.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Large Language Models for Compiler Optimization

Reference 12

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Source-reported events for the cited work

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Observation 82f98701-1406-4145-a470-f0256a605e49 · outbound

This paper cites SAT -based local improvement for finding tree decompositions of small width.

Extracting Problem Structure with LLMs for Optimized SAT Local Search SAT -based local improvement for finding tree decompositions of small width

Reference 13

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Source-reported events for the cited work

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Observation 13914a02-4889-49a6-af51-9120d58d81b5 · outbound

This paper cites The silent (r)evolution of SAT.

Extracting Problem Structure with LLMs for Optimized SAT Local Search The silent (r)evolution of SAT

Reference 14

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Source-reported events for the cited work

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Observation 0a394338-a6c3-4d7e-b9d5-9f290a5c62f0 · outbound

This paper cites Another look at graph coloring via propositional satisfiability.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Another look at graph coloring via propositional satisfiability

Reference 15

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Observation dc5c49fb-1f64-4c86-9b36-8ccf5b1c02d3 · outbound

This paper cites The PACE 2022 parameterized algorithms and computational experiments challenge: Directed feedback vertex set.

Extracting Problem Structure with LLMs for Optimized SAT Local Search The PACE 2022 parameterized algorithms and computational experiments challenge: Directed feedback vertex set

Reference 16

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.354385Z

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Observation b2a91d8e-54cd-4cdd-8296-ced5be8905f8 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search Unresolved cited work

Reference 17

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Observation d0177a5d-2306-437e-80fd-97c9868a6b6a · outbound

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

Extracting Problem Structure with LLMs for Optimized SAT Local Search PySAT: A Python toolkit for prototyping with SAT oracles

Reference 18

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Source-reported events for the cited work

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Observation c6b3c2ef-004e-4f95-83f3-542ef86ba10f · outbound

This paper cites Towards universally accessible SAT technology.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Towards universally accessible SAT technology

Reference 19

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Source-reported events for the cited work

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Observation 054f5fb3-c946-4090-a133-78c388e655cd · outbound

This paper cites On the quest for an acyclic graph.

Extracting Problem Structure with LLMs for Optimized SAT Local Search On the quest for an acyclic graph

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.873071Z

Source-reported events for the cited work

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Observation bfbd3c9c-6bb9-450b-9ddd-d99a374a67c7 · outbound

This paper cites A Survey on Large Language Models for Code Generation.

Extracting Problem Structure with LLMs for Optimized SAT Local Search A Survey on Large Language Models for Code Generation

Reference 21

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Observation f011eec6-b8ef-4279-a2db-a88222f52832 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search Unresolved cited work

Reference 22

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Source-reported events for the cited work

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Observation 4b48afd9-e693-4257-8365-96d0bb416c60 · outbound

This paper cites PACE solver description: Dager - cutting out cycles with maxsat.

Extracting Problem Structure with LLMs for Optimized SAT Local Search PACE solver description: Dager - cutting out cycles with maxsat

Reference 23

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.273674Z

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Observation 3799790c-6013-420d-8e7f-a1fab630a40d · outbound

This paper cites A dynamic MaxSAT -based approach to directed feedback vertex sets.

Extracting Problem Structure with LLMs for Optimized SAT Local Search A dynamic MaxSAT -based approach to directed feedback vertex sets

Reference 24

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.255093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9f082213-d1ac-4f27-92e7-40b65f3326af · outbound

This paper cites Satisfying versus falsifying in local search for satisfiability - (poster presentation).

Extracting Problem Structure with LLMs for Optimized SAT Local Search Satisfying versus falsifying in local search for satisfiability - (poster presentation)

Reference 25

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Observation 40e04e5d-690e-46fa-9094-164b08ee19ce · outbound

This paper cites Competition-Level Code Generation with AlphaCode.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Competition-Level Code Generation with AlphaCode

Reference 26

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Observation c773b4d7-cced-42c2-8dd7-d3f73669778c · outbound

This paper cites Conflict-driven clause learning SAT solvers.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Conflict-driven clause learning SAT solvers

Reference 27

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Observation e72b9e23-d7a2-4611-8dcf-e2844333a751 · outbound

This paper cites McAllester, Bart Selman, and Henry A.

Extracting Problem Structure with LLMs for Optimized SAT Local Search McAllester, Bart Selman, and Henry A

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.857228Z

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Observation 997c2e5b-1706-4348-8a31-293ccb9b95a5 · outbound

This paper cites Learning optimal decision trees with SAT.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Learning optimal decision trees with SAT

Reference 29

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6629eb6-c280-4806-bc40-fed504b10acf · outbound

This paper cites Codegen: An open large language model for code with multi-turn program synthesis.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Codegen: An open large language model for code with multi-turn program synthesis

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.839586Z

Source-reported events for the cited work

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Observation f3def39e-c4ce-41dc-b560-4bed0b38c10c · outbound

This paper cites Olson, William La Cava, Patryk Orzechowski, Ryan J.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Olson, William La Cava, Patryk Orzechowski, Ryan J

Reference 31

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Observation 16a6ee61-427a-4846-9836-20eceee10e01 · outbound

This paper cites SAT -based decision tree learning for large data sets.

Extracting Problem Structure with LLMs for Optimized SAT Local Search SAT -based decision tree learning for large data sets

Reference 32

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.177014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 906ad2bc-30fa-4e7b-8d82-8176effd03dd · outbound

This paper cites Extracting problem structure with LLMs for optimized SAT local search, January 2025.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Extracting problem structure with LLMs for optimized SAT local search, January 2025

Reference 33

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Observation 767804c5-9f2f-4041-a9e0-c42990fde771 · outbound

This paper cites Levesque, and David G.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Levesque, and David G

Reference 34

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Observation 3a7fa4c8-144b-48af-82bf-8315a4f888c7 · outbound

This paper cites Kautz, and Bram Cohen.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Kautz, and Bram Cohen

Reference 35

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Observation 881a462c-bd14-4bfc-aee1-8ee00b094a73 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search Unresolved cited work

Reference 36

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e95e8fb-e971-4f3e-b328-7fb71f4d901e · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search McIlraith

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dedcc232-717d-4e79-826a-a6fa43a9624a · outbound

This paper cites Marques Silva and Karem A.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Marques Silva and Karem A

Reference 38

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Observation 5d575011-cc15-4985-98ca-80e31aef8769 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search Cryptominisat 5.6.8

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.782431Z

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Observation 6bb66234-fc2d-4e57-bb0f-57b470b45597 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search A solution-driven multilevel approach for graph coloring

Reference 40

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Observation 94c721e5-c877-4918-bf96-a1c20b1a36fa · outbound

This paper cites MCP-Solver: Integrating Language Models with Constraint Programming Systems.

Extracting Problem Structure with LLMs for Optimized SAT Local Search MCP-Solver: Integrating Language Models with Constraint Programming Systems

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T15:00:54.863646Z

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Observation 8cfd84c4-0ec0-429b-ab35-c76e929800ec · outbound

This paper cites Learning optimal classification trees using a binary linear program formulation.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Learning optimal classification trees using a binary linear program formulation

Reference 42

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.082715Z

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Observation d96e0730-a2d4-4aa3-9d21-ebdc2e520ab6 · outbound

This paper cites Generating streamlining constraints with large language models.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Generating streamlining constraints with large language models

Reference 43

Resolution: verified exact; doi, observed 2026-08-10T15:00:55.066220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f082920b-4d75-49aa-9682-0f7a84f8bf10 · outbound

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Extracting Problem Structure with LLMs for Optimized SAT Local Search Realtime generation of streamliners with large language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.764957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91749e61-b329-4273-96ef-a106cf1f547f · outbound

This paper cites Learning local search heuristics for boolean satisfiability.

Extracting Problem Structure with LLMs for Optimized SAT Local Search Learning local search heuristics for boolean satisfiability

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.745681Z

Source-reported events for the cited work

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Observation eb054d60-2d73-4f37-a6ad-67f27ee9efbc · outbound

This paper cites A spin glass approach to the directed feedback vertex set problem.

Extracting Problem Structure with LLMs for Optimized SAT Local Search A spin glass approach to the directed feedback vertex set problem

Reference 46

Resolution: verified exact; doi, observed 2026-08-10T15:00:54.942077Z

Source-reported events for the cited work

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Observation fb32665a-fb4f-49c1-bc9b-46e4e5e8afd0 · outbound

This paper cites write newline.

Extracting Problem Structure with LLMs for Optimized SAT Local Search write newline

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:00:55.730016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.