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VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking

Paper · 2026 · Mark Rothermel, Marcus Kornmann, Marcus Rohrbach et al.

Summary

Benchmarks for automated fact-checking (AFC) are usually frozen in time. Their claims and verdicts gradually enter the web data used to pretrain LLMs, which inflates measured performance. Many are also limited in modality, language or domain, or depend on invented claims.

The authors present VeriTaS, a dynamic multimodal benchmark. It is built only from real claims already reviewed by professional fact-checkers and covers text, images and video in dozens of languages. A seven-stage automated pipeline adds new material every quarter. It rewrites claims into a normalized form, recovers the original media, and translates the varied expert rulings into a new rating scheme. Instead of one coarse label, separate claim and media properties each get an uncertainty-aware score with a written justification. This allows MSE-based evaluation, which tolerates small errors but strongly penalizes reversed verdicts.

Human evaluation confirms that the automatic labels agree closely with human judgment. Baseline tests show that current multimodal LLMs remain far from reliable fact-checkers. Their scores on claims published before their knowledge cutoff overstate their real ability, which is the core argument for leakage-resistant evaluation.

Key points

  • Scale: about 25,000 real-world claims from 104 fact-checking organizations in 54 languages, covering textual, image and video content.
  • Scoring: properties such as claim veracity, claim integrity, video authenticity and video contextualization are rated on a −1 to 1 scale. Each rating comes with a justification, and the benchmark reports 5 scores rather than a single class.
  • Leakage evidence: models perform worse on claims that appeared after their knowledge cutoff (Fig. 5 of the paper).
  • Update commitment: the authors promise quarterly extensions until at least Q4 2028; a Q1 2026 extension has already been demonstrated.
  • Cost and access: the benchmark has cost about $16.5K so far (about $660 per quarterly split), plus roughly 2.9K GPU hours on 8 NVIDIA H100s for the in-house LLAMA 4 MAVERICK. Access for researchers is gated via veritas.mai.informatik.tu-darmstadt.de.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksSocial Sciences · 1,954 worksSociology and Political Science · 626 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1,341 worksVision & language · 811 works

Cite

Cite as Mark Rothermel, Marcus Kornmann, Marcus Rohrbach and Anna Rohrbach. 2026. VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1948

@inproceedings{rothermel2026veritas,
  title = {{VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking}},
  author = {Mark Rothermel, Marcus Kornmann, Marcus Rohrbach and Anna Rohrbach},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1948},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1948}
}

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