Pith. sign in

Paper Citation Record · LEDGER

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods

As of 15 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.13333.

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

pith.paper-citation-record.v1
2608.13333 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:30:55.685027Z

measured 25 of 25 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 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

25 of 25 outbound references displayed

  • verified exact11
  • verified fuzzy3
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ccb86d8c-340a-4725-a22b-7d0985af9d11 · outbound

This paper cites SUNNY: a lazy portfolio approach for constraint solving.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods SUNNY: a lazy portfolio approach for constraint solving

Reference 1

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.938146Z

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=2026-08-14T13:30:55.564080Z digest=sha256:04bb1d596b054b1cb938ebec10f4a32eab53609263c513e2938d1b0959669d49

Observation c93b9727-f8cf-4686-8f5e-cf5bf4ef6c6d · outbound

This paper cites Large language model meets constraint propagation.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Large language model meets constraint propagation

Reference 2

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.922845Z

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=2026-08-14T13:30:55.569428Z digest=sha256:f682b15e9ceb280b47f59888632bf64e00d98096a37913f930038616254c74bb

Observation 97999970-55af-463a-95a1-7b08f84d4e78 · outbound

This paper cites Tree clustering for constraint networks.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Tree clustering for constraint networks

Reference 3

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.906682Z

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=2026-08-14T13:30:55.574519Z digest=sha256:ee43e834a17ef099e6e1c98ee02d495392f90462bfabe71c6151b85b607ff756

Observation ff8da600-7300-4751-b078-7d6f49a8afde · outbound

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

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods SAT -based local improvement for finding tree decompositions of small width

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.580867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.580867Z digest=sha256:5ebda5e4c253a79dc7870b2efd2c4e7d371aec7bb62faef8924a5608d84d9b60

Observation 74ea18b3-c068-436e-9ce9-e97f46d29726 · outbound

This paper cites Exact combinatorial optimization with graph convolutional neural networks.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Exact combinatorial optimization with graph convolutional neural networks

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:30:56.372677Z

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=2026-08-14T13:30:55.586097Z digest=sha256:b554a7c041b8598cc99cbf22cb35d1a5f3b44b04318079ed2d5afd3a9a8ebcf8

Observation 296f5cc9-37da-4ba6-94af-fe84ca786db7 · outbound

This paper cites Hypertree decompositions and tractable queries.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Hypertree decompositions and tractable queries

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.591835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.591835Z digest=sha256:b3f766639cfe75fd5e26bb7b826497d8f54663da2419424604686f8e94b9849b

Observation 734e9375-8d38-44ab-b13a-fe2a52a81134 · outbound

This paper cites Neural large neighborhood search for routing problems.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Neural large neighborhood search for routing problems

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.597217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.597217Z digest=sha256:0172217d0e65f60cc1b8d0b2f6a0694c7366ac94c3969c504d0b354dac879697

Observation 7f21113b-0b58-4157-8da5-3167cc004680 · outbound

This paper cites GRAPH reinforcement learning for operator selection in the ALNS metaheuristic.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods GRAPH reinforcement learning for operator selection in the ALNS metaheuristic

Reference 8

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.882591Z

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=2026-08-14T13:30:55.601824Z digest=sha256:a0da3606c6176afa505f2d7a83345e4bd57dd397c680fc8bd467f5afc7d0ebd7

Observation ba406c80-172f-45f1-962b-2c9c6fad770f · outbound

This paper cites Hoos, Frank Hutter, and Torsten Schaub.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Hoos, Frank Hutter, and Torsten Schaub

Reference 9

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.866182Z

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=2026-08-14T13:30:55.606526Z digest=sha256:be4b86519704a155e7b56c44a21cbee557e95974de82615a98a046234dd9f18c

Observation 734a67bc-7a7f-4090-a081-2a54c038cc44 · outbound

This paper cites A SAT approach to branchwidth.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods A SAT approach to branchwidth

Reference 10

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.850426Z

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=2026-08-14T13:30:55.611273Z digest=sha256:a3484a2ed04333c17dd479762ae390d9276351098f78a623907231359d256fc6

Observation 1cc5f2d9-d63f-40fc-a4d3-c03e25915994 · outbound

This paper cites Learning large Bayesian networks with expert constraints.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Learning large Bayesian networks with expert constraints

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:30:56.357108Z

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=2026-08-14T13:30:55.615932Z digest=sha256:9ce82882f50798bb5afb7ed3f00e8cc4be7e8f1bc99505f74ee92f1ac8bb53cc

Observation 1e56912b-8aa0-4733-85de-9e25b84ee356 · outbound

This paper cites Large neighborhood search.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Large neighborhood search

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.620906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.620906Z digest=sha256:d52cee0faede17dfa2562899e9e01b4bef4596934a3885d90010791d8f8fe76b

Observation 4b8d116c-f3a2-4c23-813d-e3216093fe3d · outbound

This paper cites MaxSAT-Based postprocessing for treedepth.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods MaxSAT-Based postprocessing for treedepth

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.625659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.625659Z digest=sha256:70b863cae99af6bab2077c5dbc2b2458952d2e883d9bab67f59868fb872f3aed

Observation 45fedd24-ae71-412e-abe1-6dc5002c495a · outbound

This paper cites Turbocharging treewidth-bounded bayesian network structure learning.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Turbocharging treewidth-bounded bayesian network structure learning

Reference 14

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.816231Z

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=2026-08-14T13:30:55.631305Z digest=sha256:67f1611eadc34459513aaa77349cbe5281c8b92614a3a8559dc9646a45b7b57f

Observation f5d381d4-75a8-4abb-95aa-d09533e201dc · outbound

This paper cites The power of collaboration: Learning large bayesian networks at scale.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods The power of collaboration: Learning large bayesian networks at scale

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.636009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.636009Z digest=sha256:85533f017793d2cc466380a6af18221e96e656a6bdcd2462767326c029beeb3e

Observation 91490db1-959e-4d5a-bebb-8892490876e0 · outbound

This paper cites Pawan Kumar, Emilien Dupont, Francisco J.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Pawan Kumar, Emilien Dupont, Francisco J

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.640842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.640842Z digest=sha256:766187d8d02262b3758681caac7c8eff175e1e49917988da3f1e3da80d4de085

Observation 4eb6b54a-f98e-4658-85a7-544530ff881f · outbound

This paper cites An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.646327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.646327Z digest=sha256:365cde6d2abd2e1d29897d4687eb1024927cbb0e8f4a36ae68aefb8d93826247

Observation 32094757-5d6e-4e21-ac46-f78a0f6697c5 · outbound

This paper cites Sat-boosted tabu search for coloring massive graphs.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Sat-boosted tabu search for coloring massive graphs

Reference 18

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.789549Z

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=2026-08-14T13:30:55.651015Z digest=sha256:3483daced31581e98b641d948fa3f796cad60cbb1a8744c49138c9022dd701e7

Observation cfb9e0e1-9204-48b5-a668-f58b6a81cc01 · outbound

This paper cites Structure-guided local improvement for maximum satisfiability.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Structure-guided local improvement for maximum satisfiability

Reference 19

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.773572Z

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=2026-08-14T13:30:55.656381Z digest=sha256:579e17f85c3c77437b09a8953a1ea818e79b75aabdca1c6f6f3e36b004aa1617

Observation 78c9a0a1-37fd-49c9-8414-1afa8bf47deb · outbound

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

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods SAT -based decision tree learning for large data sets

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.661782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.661782Z digest=sha256:5f72f1fde8c5e34fd8d9b2941fbd600d7f605751b89b41598c9c3be45debf85f

Observation 24e688bb-9607-43fc-ad44-3a4661b537a2 · outbound

This paper cites Using constraint programming and local search methods to solve vehicle routing problems.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Using constraint programming and local search methods to solve vehicle routing problems

Reference 21

Resolution
verified exact
doi, observed 2026-08-14T13:30:55.748404Z

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=2026-08-14T13:30:55.666534Z digest=sha256:66128cca00c807d5655992ffebeb4c53ac427c7fb1d6ed911822781384041d4f

Observation 75d741e3-6c22-44f8-b6bf-02707606e1ad · outbound

This paper cites Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.671259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.671259Z digest=sha256:d6e9b93094073e4880f1d88ca0c7506a5ba77258487ad41924085ca6c35763fc

Observation 0284afe4-9ae2-4410-ba85-4c06cf8f138e · outbound

This paper cites SAT-Based tree decomposition with iterative cascading policy selection.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods SAT-Based tree decomposition with iterative cascading policy selection

Reference 23

Resolution
verified exact
doi, observed 2026-08-14T13:30:56.342314Z

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=2026-08-14T13:30:55.675880Z digest=sha256:af6fa4456caa3eb7d9a6cc5a003a846652e924ae7f04ac50bcc23e9f7018d2c7

Observation ab412a5c-7166-439d-b2e3-b5c09ac6a96e · outbound

This paper cites Hoos, and Kevin Leyton - Brown.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Hoos, and Kevin Leyton - Brown

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:55.680429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:30:55.680429Z digest=sha256:9400b898912b17505c8fc08cb4023b5348d096cf586dee49809f2aad1382e1ef

Observation c952cd8b-fa56-4a77-8f50-2811f004cf94 · outbound

This paper cites Le, Denny Zhou, and Xinyun Chen.

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods Le, Denny Zhou, and Xinyun Chen

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:30:56.326480Z

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=2026-08-14T13:30:55.685027Z digest=sha256:336bbc4df37c39822dfa6998dbac4ada72e40dce29e5887d6755981b06d7861c

Pith citing papers

No inbound Pith citation observations are available.