让推理与搜索协同工作，提升复杂假说验证的准确率。
Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification
- 用分层代理分别负责推理链构建和信息检索
- 在EX-FEVER和HOVER上显著优于现有方法
- 适合需要可解释验证过程的研究者
多跳假说验证本质上具有挑战性，需通过多步推理构建验证链条，并迭代搜索以发现隐藏的连接事实。这一过程本质是交织的：有效的推理依赖动态获取的证据，而有效的搜索则需基于部分信息进行推理以优化查询。为此，我们提出分层代理推理与信息搜索框架（HARIS），显式建模推理驱动搜索与搜索支持推理的协同机制。HARIS包含高层推理代理，专注于构建主验证链、生成事实问题；以及低层搜索代理，根据中间结果迭代检索并优化搜索。该设计使各代理专注任务，提升验证准确率与可解释性。HARIS采用基于结果的强化学习训练。在EX-FEVER和HOVER基准上的实验表明，HARIS性能优异，极大推进了多跳假说验证的发展。
原文摘要 · Abstract (English)
Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidden bridging facts. This process is fundamentally interleaved, as effective reasoning relies on dynamically retrieved evidence, while effective search demands reasoning to refine queries based on partial information. To achieve this, we propose Hierarchical Agent Reasoning and Information Search (HARIS), explicitly modeling the coordinated process of reasoning-driven searching and search-informed reasoning. HARIS consists of a high-level reasoning agent that focuses on constructing the main verification chain, generating factual questions when more information is needed, and a low-level search agent that iteratively retrieves more information, refining its search based on intermediate findings. This design allows each agent to specialize in its respective task, enhancing verification accuracy and interpretability. HARIS is trained using reinforcement learning with outcome-based rewards. Experimental results on the EX-FEVER and HOVER benchmarks demonstrate that HARIS achieves strong performance, greatly advancing multi-hop claim verification.
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