# CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction

arXiv: https://arxiv.org/abs/2605.12436 · HTML: https://mlsanity.com/paper/2605.12436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 15 of 15)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [The Automated Verification of Textual Claims (AVeriTeC) Shared Task](https://mlsanity.com/paper/2410.23850/) (2024)
- [Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking](https://mlsanity.com/paper/2502.09083/) (2025)
- [Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments](https://mlsanity.com/paper/2406.09815/) (2024)
- [Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking](https://mlsanity.com/paper/2411.05375/) (2024)
- [Loki: An Open-Source Tool for Fact Verification](https://mlsanity.com/paper/2410.01794/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims](https://mlsanity.com/paper/2410.12377/) (2024)
- [FactLens: Benchmarking Fine-Grained Fact Verification](https://mlsanity.com/paper/2411.05980/) (2024)
- [CoverBench: A Challenging Benchmark for Complex Claim Verification](https://mlsanity.com/paper/2408.03325/) (2024)
- [Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checking](https://mlsanity.com/paper/2412.15189/) (2024)
- [FACTors: A New Dataset for Studying the Fact-checking Ecosystem](https://mlsanity.com/paper/2505.09414/) (2025)
- [FinGrAct: A Framework for FINe-GRrained Evaluation of ACTionability in Explainable Automatic Fact-Checking](https://mlsanity.com/paper/2504.05229/) (2025)
- [Temporal Graph Network: Hallucination Detection in Multi-Turn Conversation](https://mlsanity.com/paper/2601.03051/) (2026)

## Abstract (arXiv)

With the vast amount of content uploaded every hour, along with the AI generated content that can include hallucinations, Automated Fact-Checking (AFC) has become increasingly vital, as it is infeasible for human fact-checkers to manually verify the sheer volume of information generated online. Professional fact-checkers have identified several gaps in existing AFC systems, noting a misalignment between how these systems operate and how fact-checking is performed in practice. In this paper, we introduce CAAFC (Chronological Actionable Automated Fact-Checker), a frame-work designed to bridge these gaps. It surpasses SOTA AFC and hallucination detection systems across multiple benchmark datasets. CAAFC operates on claims, conversations, and dialogues, enabling it not only to detect factual errors and hallucinations, but also to correct them by providing actionable justifications supported by primary information sources. Furthermore, CAAFC can update evidence and knowledge bases by incorporating recent and contextual information when necessary, thereby enhancing the reliability of fact verification.