Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2404.09491.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:00:57.966976Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T08:07:45.115362Z
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 0910dcc6-faaa-4010-8d8e-ed3c9f7a9cb8 · inbound
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers cites this paper.
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T23:37:15.396400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fe5358cd-5ebd-478a-b171-4b05442fb67f · inbound
AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents cites this paper.
AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:00:57.966976Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d43e82f-28fe-41c4-9d58-d610c1e213fe · 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 Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:14:57.212195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd00c0ec-7256-4a86-9613-245e7aa7dcd1 · inbound
FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data cites this paper.
FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T04:02:23.203505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 29137dae-ddb3-45a2-91fd-add00d32d6f2 · inbound
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction cites this paper.
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:42:44.919032Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5cbc801-8ff0-402e-9e7c-5d881d55d74c · inbound
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution cites this paper.
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:57:26.454654Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation de42302c-9636-44d8-8527-adf46f3800d3 · inbound
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning cites this paper.
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T08:07:45.117086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 10ca8044-0bd0-4b57-afc7-15be289eafba · inbound
Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.
Parameter-Free Encoders Remain Viable for RDB Foundation Models Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T11:47:14.742492Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a12b1bf-aec3-40d8-a8d0-9d7c3ac28224 · inbound
Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.
Parameter-Free Encoders Remain Viable for RDB Foundation Models Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T08:36:03.028181Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.