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Paper Citation Record · LEDGER

InfographicVQA

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2104.12756.

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

pith.paper-citation-record.v1
2104.12756 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:17:27.900588Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:49:18.047840Z

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 c16c13f3-b74c-486a-8ff6-11fc527d18b9 · 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 InfographicVQA

Reference 119

Resolution
verified exact
arxiv_id, observed 2026-05-17T20:33:26.875269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-17T20:33:26.613927Z digest=sha256:c0d9ee4e6b7eb9d26988e23a656ac2c461fa48042e28fad2d648561a7e99b9b7

Observation cbbfbcab-553b-455a-a67e-1aded7501099 · inbound

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends cites this paper.

Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends InfographicVQA

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-10T22:17:27.900588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:17:27.900588Z digest=sha256:ea86b3e13e44acb277881180ada938f8725afad34f4c640ca7ab39eda494378b

Observation 81917d7c-899a-4039-8374-d040546404e5 · inbound

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding cites this paper.

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding InfographicVQA

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-22T19:52:01.886765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-22T19:49:00.961388Z digest=sha256:46e2cbc321d91f724384139621723e7bb46e35ec4e97b12ec161b6ac3a2737c6

Observation 27d21c87-cfb0-4cf0-9ce8-3a1f3b7b226f · inbound

Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models cites this paper.

Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models InfographicVQA

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-22T14:21:39.835811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-22T14:19:34.622854Z digest=sha256:db54830f406d6c2b2b095f417db10875d823e6b7e52310935056a19c0f304ebb

Observation ec357329-5c77-4802-9e4d-91fd25aa5219 · inbound

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models cites this paper.

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models InfographicVQA

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-04T08:15:55.113951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T08:15:55.113951Z digest=sha256:942e157182f9eb912e03d67eff96f9b5f856f409f2f8d68e2fa707a378b4797d

Observation dc315a0f-de32-4254-b33f-c504ef33e642 · inbound

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation cites this paper.

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation InfographicVQA

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T12:06:36.929511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T12:06:36.929511Z digest=sha256:2476607601c3f8294c297148c52367584c17d258d83c2c0af14d7dbd4f654947

Observation 1a8f2ebf-1202-40bd-8ece-d5a17c6b85cd · inbound

Kimi K2.5: Visual Agentic Intelligence cites this paper.

Kimi K2.5: Visual Agentic Intelligence InfographicVQA

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-10T16:09:05.339546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T16:09:05.225767Z digest=sha256:eba8c132adfccbc4a091d68e31ca3f91e7b28a509c8363dae23cab2dbe1010db

Observation 060bf5c0-ab02-4e9d-8812-72eed8a0eecd · inbound

Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning cites this paper.

Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning InfographicVQA

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-02T19:41:33.549591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:41:33.549591Z digest=sha256:67230cca269465b5b976e556b39944308be4ebd9c045cc4413306fb9dd719e3d

Observation 6977d310-e376-4565-881f-16f8d8fec6cc · inbound

Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models cites this paper.

Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models InfographicVQA

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:03:12.121069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T20:02:44.124502Z digest=sha256:f0bdf742a842cdb7bf824c0500ebb4589fada3b1fa50bfe5ccce666221f5d462

Observation 3a7fc88e-2bdf-4c7c-ab6e-45ca67073d3d · inbound

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment cites this paper.

Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment InfographicVQA

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:41:22.806700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T17:30:39.410040Z digest=sha256:82b691167a11864c9a612f16f2c5ee36803eca0ef4690724567e1eddf8edd598

Observation a517b7d4-85fb-4cae-84c7-cb1cc995c878 · inbound

Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models cites this paper.

Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models InfographicVQA

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:16:10.969368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T17:14:11.941977Z digest=sha256:853a464b948b6b1cde825cd3933b27d611bd0b2f3d52a9cf8b2a0f1627e101b2

Observation b10619f3-6942-44e7-afb2-1f900912b75c · inbound

Enginuity: A Dataset and Benchmark for Vision-Language Understanding of Engineering Diagrams cites this paper.

Enginuity: A Dataset and Benchmark for Vision-Language Understanding of Engineering Diagrams InfographicVQA

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T02:26:26.239047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-28T10:59:51.980559Z digest=sha256:637cfa1c225f0d59586acc5cf5d5ede320ac020dd1b48f695b18527c444a4780

Observation 2a552759-cfe3-49c6-a22b-78088ce85eb0 · inbound

MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs cites this paper.

MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs InfographicVQA

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:08:43.719131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-27T04:32:04.918645Z digest=sha256:8cb73e3f99a276360164fa656653b126c428cf83773e433e866323132c7fac6f

Observation 5a9c32be-0c02-4617-93f7-9729fd6f1cfb · inbound

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models cites this paper.

PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models InfographicVQA

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:49:18.049881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T20:59:26.886235Z digest=sha256:4a3d41107c77db5ffa9b5df1e18592b437dc61774a8c10012b39e91b9622c55f

Observation c35469fc-3a31-4e53-a0a4-01e99de5bbe6 · inbound

Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning cites this paper.

Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning InfographicVQA

Reference 46

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:48:32.359688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-07-03T14:47:35.377391Z digest=sha256:722c4370ce18ff8a845bd5be9a8770c6932e01114a47bff0777e4d87a1587c41

Observation 42c284ec-8a14-49c4-8d1a-f6c3308df46d · inbound

Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model cites this paper.

Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model InfographicVQA

Reference 127

Resolution
unresolved
no resolver link, observed 2026-07-31T06:20:14.121564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T06:20:14.121564Z digest=sha256:3841145c840c5ce06719a3bc6493693eb825e3213f1463d76e9d0d20cf28d39f