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Paper Citation Record · LEDGER

TableRAG: Million-Token Table Understanding with Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.04739.

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

pith.paper-citation-record.v1
2410.04739 v3

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:14:06.831855Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:14:25.796651Z

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 dd67a8dd-4af4-4229-b675-085966f59211 · inbound

MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps cites this paper.

MRT at SemEval-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps TableRAG: Million-Token Table Understanding with Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T13:14:06.831855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:14:06.831855Z digest=sha256:47bc1972289e8ab6c9f53be99eac8009c1c07905e2ab321be8928691984366ff

Observation 656d6d4b-cfe0-4730-82bc-14b22231903f · 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 TableRAG: Million-Token Table Understanding with Language Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T21:47:39.398888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:47:39.398888Z digest=sha256:f81edf26f0b0fa3496e98f6f0b244d445d7a0ff1e659e647edd41b5a6cb91f19

Observation 87915663-d182-485e-a243-6b0df5f4326d · inbound

MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps cites this paper.

MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps TableRAG: Million-Token Table Understanding with Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T16:36:52.897588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:36:52.897588Z digest=sha256:d294f1a30f7cc3562768105db3bf0d34ac1d8c40002c1d36ee73d3f3987266bc

Observation c87c528b-9266-4159-ba6e-29ba95919574 · 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 TableRAG: Million-Token Table Understanding with Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:36:36.662201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T06:31:39.551106Z digest=sha256:36b60eb898b41ef84984015e81c7c192d139c843a6721b8c229bdb03653615ed

Observation c54b7ee1-7bc6-45fb-9037-a05b980eb00a · inbound

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning cites this paper.

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning TableRAG: Million-Token Table Understanding with Language Models

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T08:14:25.798882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T08:06:18.672841Z digest=sha256:0d533ca1eb07ff8c77bb0ea292a6d38664c9152f4a63fdae6316c3073c73ac78