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Paper Citation Record · LEDGER

Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.10873.

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

pith.paper-citation-record.v1
2407.10873 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:11:56.648739Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T05:08:05.298993Z

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 eb7a7864-916c-4d0b-b3ed-91a888f2ff09 · inbound

Explainable AI-assisted Optimization for Feynman Integral Reduction cites this paper.

Explainable AI-assisted Optimization for Feynman Integral Reduction Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-07T21:11:56.648739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:11:56.648739Z digest=sha256:763c1f28a7fa8ddc78a31c18f32076f4ec1fd1701ad95c827792ddc27de3a96d

Observation dc54e0c3-834c-49ab-9a8e-7895483bbcfe · inbound

ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract) cites this paper.

ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract) Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:08:05.302811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-20T05:03:28.824500Z digest=sha256:15befcbf0555bd4cbbbbaff79419d1db6caa47cedd989896e4962ba28ed723b1