ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation
The paper presents Tree of Evidence (ToE), a hierarchical framework for automated claim verification that builds dynamic argument trees. It combines a reinforcement learning retrieval agent, evidence evaluation, and tree aggregation to create explainable evidence chains. Experimental results show ToE outperforms existing baselines by 4 to 24 percentage points, particularly on adversarially poisoned inputs.
- ▪ToE models each claim as a dynamically expanding argument tree to facilitate hierarchical reasoning.
- ▪The system integrates a reinforcement‑learning driven multi‑source retrieval agent, an evidence evaluation agent, and an aggregation algorithm.
- ▪The authors derive a formal error bound that guarantees the learned policy converges near the information‑theoretically optimal policy.
- ▪Across multiple datasets and large language models, ToE achieves improvements ranging from 4 to 24 percentage points over competitive baselines, with strong gains on adversarially poisoned inputs.
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Computer Science > Artificial Intelligence arXiv:2606.27736 (cs) [Submitted on 26 Jun 2026] Title:ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation Authors:Zhaoqi Wang, Zijian Zhang, Kun Zheng, Zhen Li, Xin Li, Chunlei Li, Jiamou Liu View a PDF of the paper titled ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation, by Zhaoqi Wang and 6 other authors View PDF HTML (experimental) Abstract:The rapid spread of fake news poses increasing threats to information ecosystems, especially as AI-generated misinformation under Generative Engine Optimization (GEO) poisoning allows adversarially crafted content to be systematically surfaced by retrieval…
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