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Paper Citation Record · LEDGER

LLM-Select: Feature Selection with Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:11:43.758581Z

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 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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-23T03:03:09.697466Z digest=sha256:e1379f56cac95220e92fbdfdeeaa112020c303c7b19b7fbd1712c47f9a0a0278

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.

source=pdf_text observed=2026-08-07T11:11:43.758581Z digest=sha256:37beff4382bcfdba8a105b80c9f1a0438843362adf9b239db98581b43b03969c

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.

source=arxiv_source observed=2026-08-07T05:14:57.243758Z digest=sha256:b0ec8e2032fc28cb174a066c9c08440723d2b159b09b7a4a4cea65ab93740fa5

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.

source=pdf_text observed=2026-08-03T17:44:44.193945Z digest=sha256:f162ca937f0c6b8a41070c53a55ae6b47e460e3ff24b1efa40f0f0ad8cfa40cb

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.

source=pdf_text observed=2026-08-03T02:46:17.574574Z digest=sha256:ade81293c390b252622fbda42be14adbd385788e01d5d34e1557bb7a19360100

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T11:28:04.724476Z digest=sha256:c4b0060f127eca7ae6e35cda08365a24b73fa8a20fe6ef63dcb7bbfe5ed5f962

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T00:39:05.990912Z digest=sha256:303c046fde444f2f8199ba7c9e1bb4a6a95d4728194f34b93a6e151955dc22cb

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.

source=pdf_text observed=2026-08-02T09:47:30.036304Z digest=sha256:4c1cfaca5959addd641a718f9492ea84dc0abad500ebd79cfc4f6e1019c97e80