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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.27736 |
| Publication time | Mon, 29 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-29T07:20:58.013Z |
| Last seen | 2026-06-29T07:20:58.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 7z8COShp9lgv |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
Discussion
0 commentsMore from arXiv.org
