DARE: Dialectical Agentic Reasoning for Structured Knowledge Fact Checking

2026-09-12

AI Summary

本文提出DARE框架，通过迭代检索-推理-反思过程解决结构化知识事实核查中的无效关系生成、单路径推理和偏见证据评估问题。

Abstract

Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-generation approaches leverage large language models (LLMs) to generate executable graph reasoning programs, achieving strong performance on structured knowledge fact checking benchmarks. However, these methods remain limited by invalid relation generation, single-path reasoning that lacks self-correction, and biased evidence assessment that tends to overestimate supporting signals. We propose Dialectical Agentic Reasoning (DARE), a multi-agent framework that formulates structured knowledge fact checking as an iterative retrieve-reason-reflect process. DARE integrates relation-grounded evidence retrieval to constrain reasoning to valid structures, dialectical bidirectional verification to evaluate evidence from both supporting and refuting perspectives, and confidence-driven meta-reflection to dynamically determine whether additional evidence exploration is necessary. Extensive experiments demonstrate the effectiveness of DARE in structured knowledge fact checking, with an 8B backbone achieving 88.12% accuracy and matching or surpassing GPT-4o-based program-generation baselines, which attests to the efficacy of dialectical agentic reasoning in eliciting the latent reasoning capabilities of LLMs.

Problem

• structured knowledge fact checking
• program-generation approaches
• invalid relation generation
• single-path reasoning
• biased evidence assessment

Innovation

• Dialectical Agentic Reasoning
• multi-agent framework
• structured knowledge fact checking
• relation-grounded evidence retrieval
• dialectical bidirectional verification