Paper Citation Record · LEDGER

LLM-Select: Feature Selection with Large Language Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2407.02694.

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

pith.paper-citation-record.v1
2407.02694 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:42:45.167355Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T03:05:20.883906Z

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 b4de0a13-4e42-495e-a878-5a60d729a36f · inbound

Visual Error Patterns in Multi-Modal AI: A Statistical Approach cites this paper.

Visual Error Patterns in Multi-Modal AI: A Statistical Approach LLM-Select: Feature Selection with Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T11:42:45.167355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cd2d2b9-1b86-47ba-8ebd-2114921d7115 · inbound

SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems cites this paper.

SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems LLM-Select: Feature Selection with Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T17:47:44.926726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b01bd475-f8e5-4e2a-b69c-b6368e7a9176 · inbound

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response cites this paper.

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response LLM-Select: Feature Selection with Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T11:51:02.831036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3227379d-7feb-4128-9843-15439fbdf24b · inbound

Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations cites this paper.

Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations LLM-Select: Feature Selection with Large Language Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:05:20.887610Z

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 fa015a94-330d-4cb3-9c9a-7d8b874d5fa9 · inbound

FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes cites this paper.

FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes LLM-Select: Feature Selection with Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:11:43.758581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36cfa645-c0d7-4900-8dde-f39c04800e9f · 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 LLM-Select: Feature Selection with Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:57.243758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d7d0e00-ecb1-48c2-88aa-4f52806c4828 · inbound

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals cites this paper.

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals LLM-Select: Feature Selection with Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T17:44:44.193945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12f5bebc-2920-4e63-986b-ad9c84fa3249 · inbound

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection cites this paper.

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection LLM-Select: Feature Selection with Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T02:46:17.574574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aee6c89b-ed66-45a5-8aab-12b3ed578947 · inbound

Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery cites this paper.

Mamba-SSM with LLM Reasoning for Feature Selection: Faithfulness-Aware Biomarker Discovery LLM-Select: Feature Selection with Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:30:18.690978Z

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 5777546c-3762-4ebf-bed2-e18c50ff8869 · 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 LLM-Select: Feature Selection with Large Language Models

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:39:48.419463Z

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 fcca538d-f277-4105-a35d-26eeeff9f5c2 · inbound

Feature Generation Using LLMs: An Evolutionary Algorithm Approach cites this paper.

Feature Generation Using LLMs: An Evolutionary Algorithm Approach LLM-Select: Feature Selection with Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T09:47:30.036304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.