mlsanityData to 7 Oct 2026

WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models

arXiv 2602.02537 ↗First posted 28 Jan 2026cs.CV cs.LGcited by 5

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often conflate visual knowledge retrieval with reasoning, WorldVQA decouples these capabilities to strictly measure "what the model memorizes." The benchmark assesses the atomic capability of grounding and naming visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities. We expect WorldVQA to serve as a rigorous test for visual factuality, thereby establishing a standard for assessing the encyclopedic breadth and hallucination rates of current and next-generation frontier models.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 31 of its 33 references that are in the map. First posted on arXiv 28 Jan 2026.

Builds on

31 of 33 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering9 Mar
  2. 2024Are We on the Right Way for Evaluating Large Vision-Language Models?29 Mar
  3. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts3 Oct

Other references · 28

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  5. 2023MMBench: Is Your Multi-modal Model an All-around Player?cited by 2,105
  6. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
  7. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  8. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  9. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Modelscited by 1,624
  10. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
  11. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  12. 2016Modeling Context in Referring Expressionscited by 1,054
16 more
  1. 2023SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehensioncited by 929
  2. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  3. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  4. 2018Object Hallucination in Image Captioningcited by 661
  5. 2023HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Modelscited by 572
  6. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 494
  7. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 466
  8. 2024A Survey on Hallucination in Large Vision-Language Modelscited by 352
  9. 2024Measuring short-form factuality in large language modelscited by 345
  10. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  11. 2023AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluationcited by 293
  12. 2023Demystifying CLIP Datacited by 265
  13. 2025SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Modelscited by 61
  14. 2019AI2D-RST: A multimodal corpus of 1000 primary school science diagramscited by 60
  15. 2025Meta CLIP 2: A Worldwide Scaling Recipecited by 59
  16. 2021Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detectioncited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Kimi K3: Open Frontier Intelligencecited by 99
  2. 2026Beyond Language Modeling: An Exploration of Multimodal Pretrainingcited by 32
  3. 2026MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agentscited by 2
  4. 2026Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Visioncited by 1
  5. 2026FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisitioncited by 0

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  6. 2025RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks
  7. 2025"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
  8. 2025VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs