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Paper Citation Record · LEDGER

AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2402.10705.

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

pith.paper-citation-record.v1
2402.10705 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:18:49.995315Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 673a386c-bd32-4c0a-b0d7-6f0c0dd05ff3 · inbound

Discovering heuristics in a complex SAT solver with large language models cites this paper.

Discovering heuristics in a complex SAT solver with large language models AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T11:18:49.995315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:18:49.995315Z digest=sha256:c45bf8761c65f28e5892e51f1fcf5dfca97de648e6ba8852771168042c23b7c9

Observation da5ff7e8-9aba-48c2-bb34-a88bfafbae74 · inbound

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving cites this paper.

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 156

Resolution
unresolved
no resolver link, observed 2026-08-04T20:55:41.695004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:55:41.695004Z digest=sha256:8f5a14d38e119e437c51960d7736c889729923c33b64f9cc7861b606ab552350

Observation fd757683-e1cb-4f82-9d6d-d2657da3fdd7 · inbound

IC3-Evolve: Proof-/Witness-Gated Offline LLM-Driven Heuristic Evolution for IC3 Hardware Model Checking cites this paper.

IC3-Evolve: Proof-/Witness-Gated Offline LLM-Driven Heuristic Evolution for IC3 Hardware Model Checking AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-16T13:27:55.893010Z

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.

source=pdf_text observed=2026-05-16T13:23:20.746776Z digest=sha256:4f72550b8acb27a7fa7af617c70f4787908562b11c059f6d481d1a50761c9027

Observation 82fa6eac-03b7-4da3-97e4-accd82e45e03 · inbound

PyVRP$^+$: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems cites this paper.

PyVRP$^+$: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:45:49.258516Z

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.

source=pdf_text observed=2026-05-10T18:21:52.115078Z digest=sha256:fef084cd40aeecb3a50c5066ab8c601eaea59dc95b74cc184547f30237c6e0df

Observation efb82260-fce0-4bde-b0da-6067157dfacf · inbound

Agentic MIP Research: Accelerated Constraint Handler Generation cites this paper.

Agentic MIP Research: Accelerated Constraint Handler Generation AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T02:16:16.329182Z

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.

source=pdf_text observed=2026-05-12T02:12:27.170669Z digest=sha256:6b564eb181db4a80d5871815b86bc4a0bb4656ad361b93550877e17d5aa285e1

Observation 543ff213-807d-42fd-afb7-d68ec3b98a3f · inbound

An Information-Theoretic Criterion for Efficient Data Synthesis cites this paper.

An Information-Theoretic Criterion for Efficient Data Synthesis AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:19:07.349622Z

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.

source=arxiv_source observed=2026-05-20T22:16:57.983741Z digest=sha256:6d8da756a62737e3376825d92aaf3fdfbe21581209a773786a8199e5d0eea6b4

Observation 53bba090-2716-451b-8426-70e2c9dff875 · inbound

Large Language Models for Operations Research: A Comprehensive Survey cites this paper.

Large Language Models for Operations Research: A Comprehensive Survey AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-21T03:59:32.547651Z

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.

source=pdf_text observed=2026-05-21T03:56:29.983335Z digest=sha256:caf0f71e362296051e704611b43911eacd57dca689a8e93e662bf4673ec4558c

Observation 7a0c654f-cd17-4f14-9bf1-3cfb14e03f55 · inbound

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources cites this paper.

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:20:07.389894Z

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.

source=pdf_text observed=2026-06-25T20:19:30.720291Z digest=sha256:efa935e7cbef07c5f066ebaf87758d998c3a202862f3d141a82b008842ae0fbc

Observation 96964318-db12-4208-be14-3f4216845e5a · inbound

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources cites this paper.

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources AutoSAT: Automatically Optimize SAT Solvers via Large Language Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T13:19:50.722659Z

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.

source=pdf_text observed=2026-06-26T05:18:55.074710Z digest=sha256:0f40ebd8ade2ae1d737174dc4faf70bfe786c5904045d528de04756d70c9e0b1