Paper Citation Record · LEDGER

Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.10478.

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

pith.paper-citation-record.v1
2411.10478 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:43:23.942697Z

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

3
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 26bff0c4-8624-4848-b7dc-7423fb4df9ae · inbound

GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification cites this paper.

GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T15:43:23.942697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d08eeae4-c531-4a4a-b32b-5a387878bd6a · inbound

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications cites this paper.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:12.563231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33638af2-a3fa-4291-bb47-147f2d248376 · inbound

Reinforcement Learning for Machine Learning Engineering Agents cites this paper.

Reinforcement Learning for Machine Learning Engineering Agents Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.299995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 920c4d75-fe6f-488a-8116-9fcf2d7154a9 · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-05T04:50:31.914745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd7a1d6c-64ed-44c2-82a7-57b7ccde90a0 · inbound

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow cites this paper.

AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T16:45:37.087772Z

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.

Observation 9c26504a-4a45-4c6d-82fb-c9bd88a2c398 · 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 for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:39:48.428308Z

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.