@inproceedings{shukla-etal-2025-recon,
    title = "Recon, Answer, Verify: Agents in Search of Truth",
    author = "Shukla, Satyam  and
      Dutta, Himanshu  and
      Bhattacharyya, Pushpak",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-industry.167/",
    doi = "10.18653/v1/2025.emnlp-industry.167",
    pages = "2429--2448",
    ISBN = "979-8-89176-333-3",
    abstract = "Human fact-checking is too slow to meet current demands, making automatic fact-checking system an essential alternative. Evaluating such systems is challenging as existing benchmark datasets either suffer from leakage or evidence incompleteness. This limits the realism of current evaluations. We present $\textbf{Politi-Fact-Only (PFO)}$, a 5-class benchmark dataset of 2,982 political claims from politifact.com, where all post-claim analysis and annotator cues have been removed manually from evidence article. After filtration, evidence contains information available prior to the claim{'}s verification. By evaluating PFO, we see an average performance drop of $\textbf{11.39\%}$ in terms of macro-f1 compared to PFO{'}s unfiltered version. Based on the identified challenges of the existing LLM-based fact-checking system, we propose $\textbf{RAV (Recon-Answer-Verify)}$, an agentic framework with three agents, it iteratively generates and answers sub-questions to verify different aspects of the claim before finally generating the label. Unlike prior literature, we worked on reducing the follow-up question complexity by leveraging two 2 types of structured questions, which either validate a fact or inquire about a fact. RAV generalizes across both domains and label granularities, outperforming state-of-the-art methods by $\textbf{57.5\%}$ on PFO $\textit{(political, 5-class)}$ and by $\textbf{3.05\%}$ on the widely used HOVER dataset $\textit{(encyclopedic, 2-class)}$."
}