Paper Citation Record · LEDGER

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding

As of 4 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 2 inbound Pith citation observations for arXiv:2601.00264.

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

pith.paper-citation-record.v1
2601.00264 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T18:16:45.825451Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-30T12:14:15.768381Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-30T12:16:12.909640Z

Reference resolution

21 of 21 outbound references displayed

verified exact7
verified fuzzy10
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bc597dcf-ab13-4539-b04d-9a3a2270982f · outbound

This paper cites an unresolved cited work.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-16T18:18:14.727550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0e407d3b-e9d7-464d-81b7-d8e7e3840983 · outbound

This paper cites Galactica: A Large Language Model for Science.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Galactica: A Large Language Model for Science

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:18:14.451802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1a74f378-6810-4385-95cd-81db05bee24b · outbound

This paper cites InEuropean conference on computer vision, 740–755 (Springer, 2014).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding InEuropean conference on computer vision, 740–755 (Springer, 2014)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.715464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8e5bcb1f-fc12-4f27-82c4-ae33711befbe · outbound

This paper cites InThirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track(2022).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding InThirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track(2022)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.717954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 70a78771-2cd2-476c-94ff-a1cb493369eb · outbound

This paper cites SciCap: Generating Captions for Scientific Figures.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding SciCap: Generating Captions for Scientific Figures

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:18:14.425250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 71720e92-32c2-42f6-b41b-9012286569cc · outbound

This paper cites & Wang, D.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding & Wang, D

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.743646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9c3cf8c4-7658-4eb8-ab90-16e72126ca6e · outbound

This paper cites PlotQA: Reasoning over Scientific Plots.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding PlotQA: Reasoning over Scientific Plots

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:18:14.430114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 25fa5c1e-c4b2-4793-8af3-3cd6421a1e06 · outbound

This paper cites an unresolved cited work.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-16T18:18:14.722915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b66fa4ba-fe9f-492e-8800-26f6ea7493bf · outbound

This paper cites & Lee, Y.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding & Lee, Y

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.729830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c91b4384-9888-44ef-8f9c-10b5951344b7 · outbound

This paper cites an unresolved cited work.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-16T18:18:14.720531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 41468478-7528-4b0f-94fa-c14d6367d1fb · outbound

This paper cites InInternational conference on machine learning, 8748–8763 (PMLR, 2021).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding InInternational conference on machine learning, 8748–8763 (PMLR, 2021)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.713058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f00bf635-6f92-4906-8c32-96b8ee40370a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:18:14.447793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 304a464a-9858-49f0-8c85-b73fbc5ea402 · outbound

This paper cites Mineru: An open-source intelligent data extraction tool (2024).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Mineru: An open-source intelligent data extraction tool (2024)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.732718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3bcc6ae7-329c-4345-af8c-2fd4eca81b51 · outbound

This paper cites Qwen3-VL Technical Report.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Qwen3-VL Technical Report

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:18:14.439263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 333f5880-06da-4adc-9fa3-1a9c41097a34 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-16T18:18:14.443481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d1504d4b-229e-4e38-a78f-e9151c8960bf · outbound

This paper cites an unresolved cited work.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-16T18:18:14.725347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a76eba45-f6fd-428c-9faa-a7b7bb53abaa · outbound

This paper cites InProceedings of the 13th Annual Conference on Innovative Data Systems Research (CIDR)(Amsterdam, The Netherlands, 2023).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding InProceedings of the 13th Annual Conference on Innovative Data Systems Research (CIDR)(Amsterdam, The Netherlands, 2023)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.740770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 87c2ce51-4664-493b-ab96-b67b3fb0580c · outbound

This paper cites SciBERT: A Pretrained Language Model for Scientific Text.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding SciBERT: A Pretrained Language Model for Scientific Text

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:18:14.434515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47b67c87-6864-4ea0-8cb1-3c0030b55d5e · outbound

This paper cites & Toutanova, K.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding & Toutanova, K

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.738025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b2bba85c-fd17-4ad0-92a0-26028682f6d9 · outbound

This paper cites & Hoi, S.

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding & Hoi, S

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.745934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 27b4ea63-8dd4-43c7-ac09-373c374cf525 · outbound

This paper cites In2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 4548–4559 (2024).

S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding In2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 4548–4559 (2024)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T18:18:14.735434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 2a0554d5-cbbb-4a86-9a35-3b831344355e · inbound

SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence cites this paper.

SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-30T12:14:15.768381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5080817-8d77-4597-83f6-395b35b042b7 · inbound

SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context cites this paper.

SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-30T11:41:22.698508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.