ART: Adaptive Reasoning Trees for Explainable Claim Verification

• URL: http://arxiv.org/abs/2601.05455v1
• Date: Fri, 09 Jan 2026 01:01:55 GMT
• Title: ART: Adaptive Reasoning Trees for Explainable Claim Verification
• Authors: Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, Abbaas Alif Mohamed Nishar, Pranab Mohanty, Swapnil Shinde, Yue Wu,
• Abstract summary: ART (Adaptive Reasoning Trees) is a hierarchical method for claim verification.<n>An argument's strength is determined bottom-up via a pairwise tournament of its children.<n>Our findings show that ART's structured reasoning outperforms strong baselines.
• Score: 11.001890567834094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in high-stakes environments is hindered by their opacity; their outputs lack faithful explanations and cannot be effectively contested to correct errors, undermining trustworthiness. In this paper, we propose ART (Adaptive Reasoning Trees), a hierarchical method for claim verification. The process begins with a root claim, which branches into supporting and attacking child arguments. An argument's strength is determined bottom-up via a pairwise tournament of its children, adjudicated by a judge LLM, allowing a final, transparent and contestable verdict to be systematically derived which is missing in methods like Chain-of-Thought (CoT). We empirically validate ART on multiple datasets, analyzing different argument generators and comparison strategies. Our findings show that ART's structured reasoning outperforms strong baselines, establishing a new benchmark for explainable claim verification which is more reliable and ensures clarity in the overall decision making step.

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