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Paper Citation Record · LEDGER

Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.03350.

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

pith.paper-citation-record.v1
2503.03350 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:42.232890Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T03:59:32.517513Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 986d6ccd-fd48-4eb4-98ac-c36747ba80a2 · inbound

Using Reasoning Models to Generate Search Heuristics that Solve Open Instances of Combinatorial Design Problems cites this paper.

Using Reasoning Models to Generate Search Heuristics that Solve Open Instances of Combinatorial Design Problems Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T12:45:42.232890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:42.232890Z digest=sha256:f69e02b2e8913103bd66d8e873e5f6d3da0a1157f0635bf36579debf71033c3d

Observation a65d8acf-d0df-44e7-b590-5b6dd96c6b01 · inbound

EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization cites this paper.

EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T11:25:10.925887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:25:10.925887Z digest=sha256:a59a119419abcaa2701b433dba1f683c51c374119e7e70748a8588d059d8e432

Observation a454aae4-2968-4662-806b-35a6dfbb7300 · inbound

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models cites this paper.

Joint User Association and Beamforming Design for ISAC Networks with Large Language Models Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T10:20:33.566971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:20:33.566971Z digest=sha256:50c7222e3b2127e9a1d2dcb571fc2b6228dfd20d0f675a53f653e676aa44b1da

Observation 37626d68-fc3d-4970-a204-ded308668428 · inbound

CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models cites this paper.

CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:06:14.074343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T03:29:58.210045Z digest=sha256:5be0cc229028ade83b7323cb2c92ecda4bea7f2dd26dabd4a4ac3643571af86e

Observation d8fa9c3f-50ae-4331-b750-540c5a38f15b · inbound

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

Large Language Models for Operations Research: A Comprehensive Survey Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-21T03:59:32.518953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T03:56:29.983335Z digest=sha256:728275ddc747c6733425bba7670065594982479ee92b42696c78f16ced83cfb2