Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification

• URL: http://arxiv.org/abs/2506.07528v2
• Date: Thu, 31 Jul 2025 17:12:54 GMT
• Title: Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification
• Authors: Qisheng Hu, Quanyu Long, Wenya Wang,
• Abstract summary: We propose Hierarchical Agent Reasoning and Information Search (HARIS) for multi-hop claim verification.<n>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.<n> Experimental results on the EX-FEVER and HOVER benchmarks demonstrate that HARIS achieves strong performance.
• Score: 16.655011153015202
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• SIGHT: Reinforcement Learning with Self-Evidence and Information-Gain Diverse Branching for Search Agent [39.43590030917357]
  SIGHT is a framework that enhances search-based reasoning through Self-Evidence Support and Information-Gain Driven Diverse Branching.<n>SIGHT distills search results into high-fidelity evidence via SES and calculates an Information Gain score to pinpoint pivotal states.<n> Experiments on single-hop and multi-hop QA benchmarks demonstrate that SIGHT significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2026-02-12T04:16:55Z)
• Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards [60.0970117192627]
  Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents.<n>Existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process.<n>We propose textbfCitation-aware RL Rewards (CaRR), a fine-grained reward framework for deep search agents.
  arXiv  Detail & Related papers  (2026-01-09T18:57:53Z)
• SmartSearch: Process Reward-Guided Query Refinement for Search Agents [63.46067892354375]
  Large language model (LLM)-based search agents have proven promising for addressing knowledge-intensive problems.<n>Existing works largely focus on optimizing the reasoning paradigms of search agents, yet the quality of intermediate search queries during reasoning remains overlooked.<n>We introduce SmartSearch, a framework built upon two key mechanisms to mitigate this issue.
  arXiv  Detail & Related papers  (2026-01-08T12:39:05Z)
• Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search [56.78490647843876]
  Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use.<n>We propose bfM-ASK, a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context.
  arXiv  Detail & Related papers  (2026-01-08T08:13:27Z)
• FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation [0.0]
  We introduce FAIR-RAG, a novel agentic framework that transforms the standard RAG pipeline into a dynamic, evidence-driven reasoning process.<n>We conduct experiments on challenging multi-hop QA benchmarks, including HotpotQA, 2WikiMultiHopQA, and MusiQue.<n>Our work demonstrates that a structured, evidence-driven refinement process with explicit gap analysis is crucial for unlocking reliable and accurate reasoning in advanced RAG systems.
  arXiv  Detail & Related papers  (2025-10-25T15:59:33Z)
• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
  We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
  arXiv  Detail & Related papers  (2025-10-19T16:46:11Z)
• Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning [23.104182075898297]
  Reasoning-augmented search agents, such as Search-R1, are trained to reason, search, and generate the final answer iteratively.<n>We train an LLM-based search agent with the native capability of query expansion through reinforcement learning.<n>With the assistance of the squeezer model, we discover that even a small-scale 3B LLM can demonstrate a strong capability of query expansion.
  arXiv  Detail & Related papers  (2025-10-11T04:23:30Z)
• Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning [53.05161493434908]
  Claim verification with large language models (LLMs) has recently attracted growing attention, due to their strong reasoning capabilities and transparent verification processes.<n>We introduce Veri-R1, an online reinforcement learning framework that enables an LLM to interact with a search engine and to receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors.<n> Empirical results show that Veri-R1 improves joint accuracy by up to 30% and doubles the evidence score, often surpassing its larger-scale model counterparts.
  arXiv  Detail & Related papers  (2025-10-02T11:49:48Z)
• DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning [4.817888539036794]
  DynaSearcher is an innovative search agent enhanced by dynamic knowledge graphs and multi-reward reinforcement learning (RL)<n>We employ a multi-reward RL framework for fine-grained control over training objectives such as retrieval accuracy, efficiency, and response quality.<n> Experimental results demonstrate that our approach achieves state-of-the-art answer accuracy on six multi-hop question answering datasets.
  arXiv  Detail & Related papers  (2025-07-23T09:58:31Z)
• Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression [12.437936654405211]
  Growing complexity of factual claims in real-world scenarios presents significant challenges for automated fact verification systems.<n>Existing approaches often rely on static or shallow models that fail to capture the evolving structure of reasoning paths.<n>We propose a Structural Reasoning framework that explicitly models reasoning paths as structured graphs throughout both evidence retrieval and claim verification stages.
  arXiv  Detail & Related papers  (2025-06-08T10:30:36Z)
• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation [82.28147821286709]
  We propose ClueAnchor, a novel framework for enhancing Retrieval-Augmented Generation (RAG)<n>ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations.<n>Experiments show that ClueAnchor significantly outperforms prior RAG baselines in reasoning completeness and robustness.
  arXiv  Detail & Related papers  (2025-05-30T09:18:08Z)
• SEM: Reinforcement Learning for Search-Efficient Large Language Models [26.075903427834838]
  Large Language Models (LLMs) have demonstrated their capabilities not only in reasoning but also in invoking external tools.<n>Existing reinforcement learning approaches often lead to redundant search behaviors, resulting in inefficiencies and over-cost.<n>We propose SEM, a novel post-training reinforcement learning framework that explicitly trains LLMs to optimize search usage.
  arXiv  Detail & Related papers  (2025-05-12T09:45:40Z)
• Reasoning Court: Combining Reasoning, Action, and Judgment for Multi-Hop Reasoning [17.829990749622496]
  Reasoning Court (RC) is a novel framework that extends iterative reasoning-and-retrieval methods, such as ReAct, with a dedicated LLM judge.<n>RC consistently outperforms state-of-the-art few-shot prompting methods without task-specific fine-tuning.
  arXiv  Detail & Related papers  (2025-04-14T00:56:08Z)
• Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking [118.3983437282541]
  We introduce an LLM-based search assistant that adopts a new information seeking paradigm with holistically guided Monte Carlo tree search (HG-MCTS)<n>We reformulate the task as a progressive information collection process with a knowledge memory and unite an adaptive checklist with multi-perspective reward modeling in MCTS.<n>Our multi-perspective reward modeling offers both exploration and retrieval rewards, along with progress feedback that tracks completed and remaining sub-goals.
  arXiv  Detail & Related papers  (2025-02-07T08:36:39Z)
• DeepRAG: Thinking to Retrieve Step by Step for Large Language Models [92.87532210660456]
  We propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP)<n>By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step.<n> Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 26.4%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• Prompt-fused framework for Inductive Logical Query Answering [31.736934787328156]
  We propose a query-aware prompt-fused framework named Pro-QE. We show that our model successfully handles the issue of unseen entities in logical queries.
  arXiv  Detail & Related papers  (2024-03-19T11:30:30Z)
• Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
  We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability. We develop a new dataset containing ten new types of queries with features that have never been considered. Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
  arXiv  Detail & Related papers  (2023-04-14T11:35:35Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.