Paper Citation Record · LEDGER

TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2404.19205.

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

pith.paper-citation-record.v1
2404.19205 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:41.690884Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:00:09.470383Z

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 4bab5268-be5d-429b-803a-7f47669020ae · inbound

MageBench: Bridging Large Multimodal Models to Agents cites this paper.

MageBench: Bridging Large Multimodal Models to Agents TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T21:36:09.116911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4920d74-91e3-436b-8241-f3b90f43cc97 · inbound

OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning cites this paper.

OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:33:26.742622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f27c03aa-47b6-43cc-9293-62560cf2456d · inbound

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation cites this paper.

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:57:14.210681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d939ae-e693-44ef-a778-5f7e649cadc1 · inbound

ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding cites this paper.

ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:03.012678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de5b97c-292a-4899-a4b8-97de0f694c5c · inbound

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? cites this paper.

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:41.690884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 568d8766-8268-4805-853f-3411144c6ac5 · inbound

NegVQA: Can Vision Language Models Understand Negation? cites this paper.

NegVQA: Can Vision Language Models Understand Negation? TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:11.975554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf598d3-5d6c-4cf8-82c1-009119d5b73a · inbound

Multimodal Tabular Reasoning with Privileged Structured Information cites this paper.

Multimodal Tabular Reasoning with Privileged Structured Information TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:00.037855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd8432d8-1b34-4105-8488-5183a6834312 · inbound

Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes? cites this paper.

Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes? TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.335624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12d33cae-f6e2-4ee8-8fb0-29985d374f4c · inbound

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models cites this paper.

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:20:02.470277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1756e444-3c48-44d7-a5b3-05280ff6071a · inbound

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering cites this paper.

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:08:15.465959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33ecf3f2-94fa-4379-b87f-10d7e862c0df · inbound

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images cites this paper.

Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T17:42:47.602498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ff4f62fa-276d-404c-8887-bd97571c0015 · 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 TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:21:10.703242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae73ebdb-72ed-41de-b204-c49c7679b7c1 · inbound

Imagination Helps Visual Reasoning, But Not Yet in Latent Space cites this paper.

Imagination Helps Visual Reasoning, But Not Yet in Latent Space TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T20:40:30.917208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e9ff2e2-de88-4af2-8a04-25099aec2301 · inbound

Internalized Reasoning for Long-Context Visual Document Understanding cites this paper.

Internalized Reasoning for Long-Context Visual Document Understanding TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T23:53:27.838846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c6311a2-f04c-4965-81a8-ae516c25ed73 · inbound

Internalized Reasoning for Long-Context Visual Document Understanding cites this paper.

Internalized Reasoning for Long-Context Visual Document Understanding TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T15:50:49.083652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5497679c-f7e5-4a65-ab44-b53890819249 · inbound

TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables cites this paper.

TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:23:02.407152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fccf3d71-dc0b-4008-834b-4acfda3c4303 · inbound

DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates cites this paper.

DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:53:05.015085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c81e56c-5602-4cd5-9ced-0a1090c3eea6 · inbound

WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild cites this paper.

WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:41:24.535967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5cc74831-4ac8-4542-af4a-7d89b804bda7 · inbound

WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild cites this paper.

WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:10:24.122557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 337392d0-cd18-4361-8da3-3c078145457c · inbound

Large Vision-Language Models Get Lost in Attention cites this paper.

Large Vision-Language Models Get Lost in Attention TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:26:10.158675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e292b568-110b-400b-8ced-eadd77eb08b5 · inbound

TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity cites this paper.

TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:51:09.528740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c3f0f2d2-6664-48c7-b12e-7181e678fbd7 · inbound

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis cites this paper.

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:41:17.464514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 681ee8e4-7c3d-46d3-8e07-ae89f4ed72c3 · inbound

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning cites this paper.

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:26.969229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec74af5f-128f-4a78-8a06-183fb0a6397c · inbound

TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs cites this paper.

TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:07:30.631151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ba0abfc-42d6-4a56-a13f-3b19538b04bb · inbound

IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents cites this paper.

IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:17:45.025753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6df6dd23-c1fa-440b-825e-88cb2e15f5c5 · inbound

How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations cites this paper.

How Robust is OCR-Reasoning? Evaluating OCR-Reasoning Robustness of Vision-Language Models under Visual Perturbations TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:00:09.471926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50df798d-5568-4024-9353-c21fabdf5848 · inbound

SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning cites this paper.

SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-14T07:59:56.441098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36120e9-967c-433a-929e-a93860990c36 · inbound

Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning cites this paper.

Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T11:47:24.468658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64645baa-4302-497a-8263-c3fd9b080adb · inbound

What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs cites this paper.

What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T02:46:49.743614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.