Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis, Maximilian Puelma Touzel, William Garneau, Manon Gruaz, Mike Pinder +6 more
6/18/2025
cs.CLcs.AIcs.HC

No confident code implementation found for this paper.

Abstract

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{curtis2025veracity,
  title  = {Veracity: An Open-Source AI Fact-Checking System},
  author = {Taylor Lynn Curtis and Maximilian Puelma Touzel and William Garneau and Manon Gruaz and Mike Pinder and Li Wei Wang and Sukanya Krishna and Luda Cohen and Jean-François Godbout and Reihaneh Rabbany and Kellin Pelrine},
  year   = {2025},
  eprint = {2506.15794},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.15794v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2506.15794.svg)](https://codeofpaper.com/paper/2506.15794)

Discussion