Pith. sign in

Paper Citation Record · LEDGER

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.14361.

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

pith.paper-citation-record.v1
2402.14361 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:43:38.569990Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:13:44.974674Z

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 9c736e01-ac00-42bc-9cdf-9ad5599aa88c · inbound

TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering cites this paper.

TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T12:43:38.569990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:43:38.569990Z digest=sha256:cf3d7b3d5f47c55e8daf9a60971d11b3095d073d05f6d2c7ae029ea6bce93337

Observation e811de5e-1a94-4675-aec0-da6280734199 · inbound

HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data cites this paper.

HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Data OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-04T23:41:25.666891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T23:41:25.666891Z digest=sha256:86b44bd32bff3fffb4e70c84c42cf045ba9517bffd899a11a07d265f2875cc0e

Observation be282475-666b-448d-bdf5-f13193da3c4c · inbound

A Multi-Agent Approach for Claim Verification from Tabular Data Documents cites this paper.

A Multi-Agent Approach for Claim Verification from Tabular Data Documents OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T06:36:36.656863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T06:31:39.551106Z digest=sha256:f22de8f0dfc7b1cba488aa200aad721d9fb5ccabfd29b9b0fa5423c6467ddc64

Observation 69d52da6-2945-4450-9f1f-0d60c274ddd1 · inbound

Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method cites this paper.

Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-21T01:09:20.392260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-21T01:07:54.061446Z digest=sha256:384a5ad3999610e46ceba020b190a750b6774a8a6d6f2e5f600bd5204f5bfa1e

Observation 95145648-9bc5-4443-bc21-6bb4137f0163 · inbound

Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering cites this paper.

Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:13:44.976052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T17:03:46.019948Z digest=sha256:1a0deb55a6c70f76e8d401d9c504444325052879d2ff34823912ef47e6b45f0e