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Paper Citation Record · LEDGER

Large Language Models Synergize with Automated Machine Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.03727.

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

pith.paper-citation-record.v1
2405.03727 v3

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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:57.364348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T00:39:48.432673Z

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 04660837-273f-4daf-9a61-22abfce0d879 · inbound

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation cites this paper.

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation Large Language Models Synergize with Automated Machine Learning

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T05:14:57.364348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:14:57.364348Z digest=sha256:9f5a4c78cb4fc5a1e5b6fc293ca81ef5a6e5264026e4a6a518860a0d1156915f

Observation ef9880ac-ae3e-493f-9004-58993b3aac9b · inbound

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data cites this paper.

Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data Large Language Models Synergize with Automated Machine Learning

Reference 90

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:39:48.434274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-10T00:39:05.990912Z digest=sha256:d6a8ed1012c314987dc0d886f866acf379eca6276300eff5d4404491409cf93e