Paper Citation Record · LEDGER

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?

As of 12 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 4 inbound Pith citation observations for arXiv:2412.02368.

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

pith.paper-citation-record.v1
2412.02368 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:36:24.852757Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:19:03.335574Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:54:20.682844Z

Reference resolution

60 of 60 outbound references displayed

verified exact4
verified fuzzy16
unresolved39
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5372279-12b2-4b0a-95e3-63ce7951c627 · outbound

This paper cites Automatikz: Text-guided synthesis of scientific vector graphics with tikz.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Automatikz: Text-guided synthesis of scientific vector graphics with tikz

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.602380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 661d87b5-b107-46a6-8f3e-344ef429b8df · outbound

This paper cites DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.586355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b372f0-0d0a-4809-bdd9-20ef8b921b47 · outbound

This paper cites Scene text visual question answering.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Scene text visual question answering

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.594425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b8e1369e-e701-4757-b420-93dc7a76d9da · outbound

This paper cites Elicit: Language models as research tools.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Elicit: Language models as research tools

Reference 4

Resolution: verified exact; doi, observed 2026-08-11T23:36:24.958657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c122e92b-403c-4fd4-9363-a2db1914d52b · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.594724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef81c52e-34e9-40e8-87fb-e3d08db2bbe1 · outbound

This paper cites Transformers go for the LOL s: Generating (humourous) titles from scientific abstracts end-to-end.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Transformers go for the LOL s: Generating (humourous) titles from scientific abstracts end-to-end

Reference 6

Resolution: verified exact; doi, observed 2026-08-11T23:36:24.951208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70b5358e-5c68-488a-acfa-67c1e082bdc4 · outbound

This paper cites Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.586914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a8ae3c3-3b0d-4f63-a50a-4f3a9c678043 · outbound

This paper cites Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.603625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb9af1da-ee0c-4a7f-90fe-2960baab0669 · outbound

This paper cites Exams-v: A multi-discipline multilingual multimodal exam benchmark for evaluating vision language models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Exams-v: A multi-discipline multilingual multimodal exam benchmark for evaluating vision language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.574864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 07437ee4-269f-4035-b987-8e24aa871502 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Imagenet: A large-scale hierarchical image database

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.611608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed76c6a-7ea1-43c3-8341-31fa89ae0b0e · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.614395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7945bba2-7c8f-43b0-b9c0-8be88271c234 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.617536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f787e6ca-3b28-449a-9e3d-edbaf58ea912 · outbound

This paper cites CLIPS core: A reference-free evaluation metric for image captioning.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? CLIPS core: A reference-free evaluation metric for image captioning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.620687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3b40108-ae91-4a10-8f24-37c94459b2f7 · outbound

This paper cites T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.623681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afe80c1e-7d9e-4037-bd1b-ae7277da6592 · outbound

This paper cites Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.626213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 261e4018-77df-49e3-ac78-748fb089be3a · outbound

This paper cites Learning multiple layers of features from tiny images.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Learning multiple layers of features from tiny images

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.629335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56486e10-e646-4248-8dfb-7633f3915b8d · outbound

This paper cites West, and Bill Howe.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? West, and Bill Howe

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.557982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c742c38a-ca43-4ae6-b5cd-3cef0c8e6df2 · outbound

This paper cites Holistic evaluation of text-to-image models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Holistic evaluation of text-to-image models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.549898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4065fcbb-6140-4768-80a3-d1444fff5df0 · outbound

This paper cites PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.712170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5c09fb1-54bf-423b-9565-843611a298aa · outbound

This paper cites The E val4 NLP 2023 shared task on prompting large language models as explainable metrics.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? The E val4 NLP 2023 shared task on prompting large language models as explainable metrics

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.715531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 601ddb87-e901-45fb-aa69-054dc81ebdb7 · outbound

This paper cites Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.718590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eda8e9c-c886-4254-a71d-24491027ff13 · outbound

This paper cites MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.722836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7f6339b-00bf-495b-8543-e0446f83a03a · outbound

This paper cites an unresolved cited work.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:36:25.541809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c95bd47-95a3-4194-87cb-380e7dec5d08 · outbound

This paper cites Microsoft coco: Common objects in context.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Microsoft coco: Common objects in context

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.728607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62b7b0d7-f757-4fcd-90b3-606beac84412 · outbound

This paper cites Figurefirst: A layout-first approach for scientific figures.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Figurefirst: A layout-first approach for scientific figures

Reference 25

Resolution: verified exact; doi, observed 2026-08-11T23:36:24.932781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 69122b81-1fe2-49af-85bd-5b64ed6f81de · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.734072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8faf7e59-5c32-495b-abf5-dd27bb19d193 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.527485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7e9585a5-dfb1-4cf9-82fa-09a236012bc5 · outbound

This paper cites Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.518354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eb18100c-eed6-430b-a3fd-c97b21c86cb0 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.742162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1125bea-1dd7-4c59-a8c5-d20b99b7276f · outbound

This paper cites State of What Art? A Call for Multi-Prompt LLM Evaluation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? State of What Art? A Call for Multi-Prompt LLM Evaluation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.744889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c32e0fe4-2bdf-4e5c-b6cf-6c2597bb6c0f · outbound

This paper cites Evaluating large language models for structured science summarization in the open research knowledge graph.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Evaluating large language models for structured science summarization in the open research knowledge graph

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.747047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8fd8537-aa7e-47e4-8735-70dad3f503c5 · outbound

This paper cites Introducing openai o1 (preview).

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Introducing openai o1 (preview)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.509261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 93aa862c-c758-4532-8ab0-5fb96977256b · outbound

This paper cites Zero-Shot Text-to-Image Generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Zero-Shot Text-to-Image Generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.751632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce14d11-9b2e-4f13-96b3-afe3728de606 · outbound

This paper cites SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.754369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4c579dd-bdd0-4f06-a76b-dcbcce905dd6 · outbound

This paper cites Ocr-vqgan: Taming text-within-image generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Ocr-vqgan: Taming text-within-image generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.500047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a53ccde3-da8e-44e1-bcad-a2c239059785 · outbound

This paper cites Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2).

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.759801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1c67c02-191b-4eec-ac8a-825d45a73b44 · outbound

This paper cites Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.762652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8339022e-882e-42c5-8e43-30b00e4c53fd · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.765471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c211054-9ab3-4b9d-a2da-813c14d3d249 · outbound

This paper cites ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.768284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c77b8d-fda4-4811-ae56-944a4fcbd4ee · outbound

This paper cites Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.771267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a9f03a6-efa5-4f09-8288-b1a2a5634fc3 · outbound

This paper cites Towards vqa models that can read.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Towards vqa models that can read

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.488831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 652b5bc1-f02f-4ac5-8b8b-675c166aaf60 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Gemma: Open Models Based on Gemini Research and Technology

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.776853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e10d8a00-7504-4c61-ae91-60687ef018cc · outbound

This paper cites Winoground: Probing vision and language models for visio-linguistic compositionality.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Winoground: Probing vision and language models for visio-linguistic compositionality

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.780407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e37139c-7570-4237-af61-b7157016c499 · outbound

This paper cites Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.783054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47dad0a8-ded6-4426-b2ae-bfdabd94f879 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? LLaMA: Open and Efficient Foundation Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.786161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a39e7aa2-91d3-4b09-a154-17c5a68cdf70 · outbound

This paper cites Plots made quickly: An efficient approach for generating visualizations from natural language queries.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Plots made quickly: An efficient approach for generating visualizations from natural language queries

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.474081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1859436c-9ba3-4901-92fc-0cacfaa5973f · outbound

This paper cites Contextual: Evaluating context-sensitive text-rich visual reasoning in large multimodal models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Contextual: Evaluating context-sensitive text-rich visual reasoning in large multimodal models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.465937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 34ec3669-99c7-447b-9a7c-3238a9de9f3f · outbound

This paper cites Scibench: Evaluating college-level scientific problem-solving abilities of large language models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Scibench: Evaluating college-level scientific problem-solving abilities of large language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.457618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f031aa26-6506-42b8-babb-7d68b49cb617 · outbound

This paper cites CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.796788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280d9344-caab-4daf-958c-6f8f7b06dd35 · outbound

This paper cites seaborn: statistical data visualization.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? seaborn: statistical data visualization

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.799390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96bdf0c0-9074-46f8-87a8-f9e5f2768fa0 · outbound

This paper cites Evaluating and analyzing relationship hallucinations in large vision-language models.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Evaluating and analyzing relationship hallucinations in large vision-language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.449624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 804bfb6d-e949-4db8-9e41-b897d6bfa0fc · outbound

This paper cites An automatic graph generation method for scholarly papers based on table structure analysis.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? An automatic graph generation method for scholarly papers based on table structure analysis

Reference 52

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T23:36:25.142576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 56badd52-6031-4f40-be3d-4bd11ee4d2ff · outbound

This paper cites Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:36:25.441426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 330f4ae8-8e4a-49b0-af2b-331f6281e39f · outbound

This paper cites Mm-vet: Evaluating large multimodal models for integrated capabilities.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Mm-vet: Evaluating large multimodal models for integrated capabilities

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.808524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5c4a680-b8d6-41f9-af77-8246d708752a · outbound

This paper cites MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.810662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6efe08f8-6c48-428d-860e-93166fb6f430 · outbound

This paper cites VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.813015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f83bdc48-8871-4b57-83c1-8fd35ba4ae31 · outbound

This paper cites write newline.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? write newline

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.815914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f72e5de-83b8-4647-8246-9b592ddc57b3 · outbound

This paper cites @esa (Ref.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? @esa (Ref

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.819576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8b12fa-ce42-423d-815a-2323c93d2a44 · outbound

This paper cites an unresolved cited work.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T23:36:24.822587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38d3007-f889-430b-b41f-c538f42b203b · outbound

This paper cites !1A Qa.

ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation? !1A Qa

Reference 61

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:36:25.047164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 7f637f93-847b-483a-a77e-e272dc0cf8d2 · inbound

SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model cites this paper.

SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:19:03.335574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b3e74ec-ec27-4832-9a1c-3f71c099d409 · inbound

Modeling Human Perspectives with Socio-Demographic Representations cites this paper.

Modeling Human Perspectives with Socio-Demographic Representations ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:11:20.843879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79eff9a3-0a08-4a2d-9908-10ce9e71b0e3 · inbound

Quantifying and Predicting Disagreement in Graded Human Ratings cites this paper.

Quantifying and Predicting Disagreement in Graded Human Ratings ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:11:08.840823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ac12c993-210a-4983-a4e0-6ff6f93ff381 · inbound

SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation cites this paper.

SciIR: A Large-scale Training Dataset and Benchmark for Scientific Image Reasoning Generation ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:20.684407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.