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Paper Citation Record · LEDGER

Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.06664.

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

pith.paper-citation-record.v1
2503.06664 v1

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

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:12.867763Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:29:02.927754Z

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 15b150a2-7b97-4d55-8344-31059177e7d9 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-07T14:33:12.867763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:33:12.867763Z digest=sha256:541c5ed062b22a93b2921a33b7fb388c52b848af4abc9c7b8d8625425c16bc7c

Observation b49b0a67-8f9d-4605-8bc0-3af9bf940daa · inbound

Reinforcement Learning for Machine Learning Engineering Agents cites this paper.

Reinforcement Learning for Machine Learning Engineering Agents Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T12:24:02.289207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:24:02.289207Z digest=sha256:ea3402565cdbe0a860fb382b4fcdb993bf438dedfa37fa304efb595e4bc6e414

Observation b2398e06-5385-4122-87ae-c93382834ac4 · inbound

Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning cites this paper.

Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T11:39:47.596846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:39:47.596846Z digest=sha256:53cfa5927300eafc11d0af916595c9e40d4b7656b2ee36a5f8adf5cfa1e07d25

Observation 52a3fd40-ba91-4a21-9217-12ba4d9f1dcb · 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 Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-21T22:05:41.550771Z

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=pdf_text observed=2026-05-21T22:05:11.146329Z digest=sha256:9356fa7ff6867b719e2dfe739cad5f980422a51e208ef9ebb0a18606dab3a4d6

Observation 49c1db62-1fc0-45d3-b2e1-e5a4e0a71ff4 · inbound

Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection cites this paper.

Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T18:04:28.690379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:04:28.690379Z digest=sha256:598b5ec93d8dbe3018f278b41b8ff1f230344f252f8695e4a9153eef8ce45330

Observation b13ba6f0-1bab-4db8-ba5d-4856c100901e · inbound

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:57:22.529249Z

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=pdf_text observed=2026-05-13T05:56:36.312877Z digest=sha256:fb497ed042cdf8f380f362d64d1f0dc4a768bcd54e7360c8dd95e9c603f02e1a

Observation 2c276aa2-2c80-4295-91df-6227f0383eca · inbound

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows cites this paper.

ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T14:05:46.695886Z

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=pdf_text observed=2026-06-30T22:18:45.189576Z digest=sha256:0d350779cfcb7ac3d43add9f92a93c31dd60337c43c36ecec57bd98345b9ecaf

Observation 76d208c7-8c53-412e-9b4b-f2d65a7fc43b · 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 Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:29:02.929627Z

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=pdf_text observed=2026-06-26T22:07:45.909694Z digest=sha256:1c8f2ef2220e06d824b87b926d03726cefe894c0413bc9ba678f157efee76d92

Observation 65950c18-b38c-4e28-bc55-32d2c1c5becb · inbound

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering cites this paper.

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-31T01:39:45.979523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T01:39:45.979523Z digest=sha256:1718868d3cbc601d2abdc46d156e9fce239e59e6b3de9ff86fa70ac6669cc07a