用结构化状态图提升大模型的反向推理能力，解决逻辑漏洞问题。
Graph of States: Solving Abductive Tasks with Large Language Models
- 构建状态图与状态机，约束推理过程
- 在真实数据集上显著优于基线方法
- 适合需要严谨逻辑推断的任务场景
逻辑推理包含演绎、归纳和反演。尽管大语言模型（LLMs）已有效掌握前两者，但反演推理仍严重缺乏研究。现有框架多针对静态演绎任务设计，因状态表示不结构化且缺乏显式状态控制，难以推广至反演推理，导致证据虚构、上下文漂移、回溯失败和提前终止等问题。为此，我们提出图状状态（Graph of States, GoS），一种面向反演任务的通用神经符号框架。GoS将多智能体协作建立在结构化信念状态之上，利用因果图显式编码逻辑依赖关系，通过状态机控制推理过程的有效转移。通过动态对齐推理焦点与这些符号约束，该方法将无序、不受控的探索转化为收敛、有方向的搜索。在两个真实数据集上的广泛评估表明，GoS显著优于所有基线，为复杂反演任务提供了稳健解决方案。代码与提示：https://github.com/gaorch85/Graph-of-States。
原文摘要 · Abstract (English)
Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize to abductive reasoning due to unstructured state representation and lack of explicit state control. Consequently, they are inevitably prone to Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping. To bridge this gap, we introduce Graph of States (GoS), a general-purpose neuro-symbolic framework tailored for abductive tasks. GoS grounds multi-agent collaboration in a structured belief states, utilizing a causal graph to explicitly encode logical dependencies and a state machine to govern the valid transitions of the reasoning process. By dynamically aligning the reasoning focus with these symbolic constraints, our approach transforms aimless, unconstrained exploration into a convergent, directed search. Extensive evaluations on two real-world datasets demonstrate that GoS significantly outperforms all baselines, providing a robust solution for complex abductive tasks. Code repo and all prompts: https://github.com/gaorch85/Graph-of-States.
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