Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.01645.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:57.250658Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T07:21:26.625526Z
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 87776f40-e0a5-4052-858e-3ae9497eb070 · 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 Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:14:57.250658Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 19437ab6-577c-4d6b-bcad-6c4499513894 · inbound
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:51:06.354972Z
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.
Observation 145435b0-898a-43be-8528-7609e6ff8ea0 · inbound
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification cites this paper.
BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T07:21:26.634304Z
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.