Paper Citation Record · LEDGER

Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.03403.

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

pith.paper-citation-record.v1
2305.03403 v5

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 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:18:50.480306Z

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

7
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 45326256-50c5-470f-a46e-7afd779d0f83 · inbound

SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning cites this paper.

SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:35.444743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7452ab75-a7b2-4021-98b5-0ecba84b1018 · inbound

Anchoring Trends: Mitigating Social Media Popularity Prediction Drift via Feature Clustering and Expansion cites this paper.

Anchoring Trends: Mitigating Social Media Popularity Prediction Drift via Feature Clustering and Expansion Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:00:10.007792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5bec6d2-300a-41d6-baf1-471704fef336 · inbound

In-Context Decision Making for Optimizing Complex AutoML Pipelines cites this paper.

In-Context Decision Making for Optimizing Complex AutoML Pipelines Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:41:53.728672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cd95322-5aa3-447a-9944-6de69c4c312b · inbound

Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects cites this paper.

Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:38.298737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fc1590b-98ac-4879-890a-00c36691a332 · inbound

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data cites this paper.

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:05:41.546543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation efe9d4e3-0373-42a3-a2bd-bfb099f780f8 · inbound

AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.

AgentGA: Evolving Code Solutions in Agent-Seed Space Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:00:20.987367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ccdf1653-4d9d-4038-bbdf-006cd751a3cc · inbound

AgentGA: Evolving Code Solutions in Agent-Seed Space cites this paper.

AgentGA: Evolving Code Solutions in Agent-Seed Space Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:16:20.132714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 06fb30e8-5a6f-4287-9872-b6b1d1a4de1d · inbound

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization cites this paper.

Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:29:02.918096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1e9ea0de-325b-4daa-a548-b37c91eb9a22 · inbound

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters cites this paper.

When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T18:18:50.480306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.