Paper Citation Record · LEDGER

Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2301.13808.

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

pith.paper-citation-record.v1
2301.13808 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:22.688150Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T16:22:51.164196Z

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 a530ee05-13a1-4162-9676-8a575ef9cefd · inbound

LLM Inference Enhanced by External Knowledge: A Survey cites this paper.

LLM Inference Enhanced by External Knowledge: A Survey Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:22.688150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 303d8935-24e5-4e06-99da-cfb72b5950b2 · inbound

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience cites this paper.

Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:01.655644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4dccc7d-63ca-4b1d-9ddb-dcc61a19ec30 · inbound

What to Keep and What to Drop: Adaptive Table Filtering Framework cites this paper.

What to Keep and What to Drop: Adaptive Table Filtering Framework Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:41.706145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a0b4738-54fe-4e94-b6b0-037775c0580c · inbound

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges cites this paper.

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:37.485930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 645dccf7-76ba-44c4-80db-866382fed7be · inbound

T-REX: Table -- Refute or Entail eXplainer cites this paper.

T-REX: Table -- Refute or Entail eXplainer Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T22:41:06.908153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51ef407c-a18d-48b2-82a3-ca162a7c8d5b · inbound

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding cites this paper.

Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:51.170253Z

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.