验证图：基于有向无环图的结构化LLM推理验证
Graph of Verification: Structured Verification of LLM Reasoning with Directed Acyclic Graphs
AI总结：
本文提出验证图（GoV），通过灵活的节点块架构实现适应性多粒度验证，有效解决LLM推理验证中的精度与鲁棒性平衡问题。
摘要：
验证大型语言模型（LLM）的复杂且多步骤推理是一个关键挑战，因为整体方法往往忽视局部缺陷。逐步验证是一种有前途的替代方法，但现有方法往往过于僵化。它们难以适应多样化的推理结构，从形式证明到非正式的自然语言叙述。为了解决这一适应性缺口，我们提出了验证图（GoV），一种新的适应性且多粒度的验证框架。GoV的核心创新是其灵活的
英文摘要：
Verifying the complex and multi-step reasoning of Large Language Models (LLMs) is a critical challenge, as holistic methods often overlook localized flaws. Step-by-step validation is a promising alternative, yet existing methods are often rigid. They struggle to adapt to diverse reasoning structures, from formal proofs to informal natural language narratives. To address this adaptability gap, we propose the Graph of Verification (GoV), a novel framework for adaptable and multi-granular verification. GoV's core innovation is its flexible "node block" architecture. This mechanism allows GoV to adaptively adjust its verification granularity--from atomic steps for formal tasks to entire paragraphs for natural language--to match the native structure of the reasoning process. This flexibility allows GoV to resolve the fundamental trade-off between verification precision and robustness. Experiments on both well-structured and loosely-structured benchmarks demonstrate GoV's versatility. The results show that GoV's adaptive approach significantly outperforms both holistic baselines and other state-of-the-art decomposition-based methods, establishing a new standard for training-free reasoning verification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
