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Paper Citation Record · LEDGER

Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.08516.

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

pith.paper-citation-record.v1
2411.08516 v1

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 2026-08-07T06:03:43.983044Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T22:22:43.456184Z

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 b291bf0e-5e3c-4444-a80c-da93f3c05e22 · inbound

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models cites this paper.

Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T06:03:43.983044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:03:43.983044Z digest=sha256:e53d354e8ac811d6884452a481c260f82a7cc1760400eb24a02db94a42fba62d

Observation 28ef8c85-ff4b-46b7-bfc6-aa809a3b04da · inbound

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation cites this paper.

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-18T09:21:10.711991Z

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=2026-05-18T09:17:00.389716Z digest=sha256:773a3bfec74f474b224fa6455b8c3758187f7a5e7258f8e0504e1f206b05b9f9

Observation 23e2c592-1539-466a-9e5d-48c9b6f17134 · inbound

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization cites this paper.

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T14:01:04.277987Z

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=2026-05-09T23:48:32.613988Z digest=sha256:4a21935bcab5242dcf19212ad47742536c9e1afc329cf6303c16a066a0d58e9b

Observation cdd2f969-4284-4342-a1ed-a34b600ecd22 · inbound

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring cites this paper.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T05:45:22.048112Z

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=2026-05-08T19:36:05.119054Z digest=sha256:c2ac3b3472d1daa23ec6b6b60d6e3df97104ae002a07d5573fae541d03dfe895

Observation 0e5e9e34-e8bf-45fd-aa19-6e022e4f18b3 · inbound

Position: How can Graphs Help Large Language Models? cites this paper.

Position: How can Graphs Help Large Language Models? Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 106

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T06:10:43.051745Z

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=2026-05-08T18:48:03.257015Z digest=sha256:c49cb771f5374b10253fb3f703c26afa1dae6b007a91f1916fad5a3d7427fc38

Observation f2d71b90-c979-460b-a4cd-ef6bcfa15145 · inbound

Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models cites this paper.

Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-28T22:22:43.458423Z

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=2026-06-28T22:20:42.780296Z digest=sha256:e291779e12aa9394130c690f7a5ef23b917922158517e87b9ed125a4775ee57a