矩阵作为计划：基于反馈驱动的重计划的结构化逻辑推理
Matrix as Plan: Structured Logical Reasoning with Feedback-Driven Replanning
AI总结：
MatrixCoT通过引入矩阵基础计划和反馈驱动重计划机制，提升LLM在复杂符号推理任务中的鲁棒性和可解释性。
摘要：
随着网络上的知识和语义日益复杂，增强大语言模型（LLMs）的理解和推理能力变得尤为重要。链式思维（CoT）提示已被证明能增强LLMs的推理能力。然而，它在依赖符号表达和严格演绎规则的逻辑推理任务上仍显不足。神经符号方法通过外部求解器强制正式正确性来弥补这一差距。然而，这些求解器对格式高度敏感，模型输出中的微小不稳定可能导致频繁的处理失败。LLM驱动的方法避免了解析的脆弱性，但缺乏结构化的表示和过程层面的错误纠正机制。为了进一步增强LLMs的逻辑推理能力，我们提出了MatrixCoT，一种带有矩阵基础计划的结构化CoT框架。具体而言，我们标准化和类型化自然语言表达，并附加明确的引用字段，并引入基于矩阵的规划方法以保持步骤间的全局关系。因此，计划成为可验证的产物，执行变得更加稳定。为了验证，我们还添加了反馈驱动的重计划机制。在语义等价约束下，它识别遗漏和缺陷，重写和压缩依赖矩阵，并产生更可信赖的最终答案。在五个逻辑推理基准和五个LLM上的实验表明，MatrixCoT在不依赖外部求解器的情况下，增强了LLMs在复杂符号推理任务中的鲁棒性和可解释性，同时保持了竞争性的性能。
英文摘要：
As knowledge and semantics on the web grow increasingly complex, enhancing Large Language Models (LLMs)' comprehension and reasoning capabilities has become particularly important. Chain-of-Thought (CoT) prompting has been shown to enhance the reasoning capabilities of LLMs. However, it still falls short on logical reasoning tasks that rely on symbolic expressions and strict deductive rules. Neuro-symbolic methods address this gap by enforcing formal correctness through external solvers. Yet these solvers are highly format-sensitive, and small instabilities in model outputs can lead to frequent processing failures. The LLM-driven approaches avoid parsing brittleness, but they lack structured representations and process-level error-correction mechanisms. To further enhance the logical reasoning capabilities of LLMs, we propose MatrixCoT, a structured CoT framework with a matrix-based plan. Specifically, we normalize and type natural language expressions and attach explicit citation fields, and introduce a matrix-based planning method to preserve global relations among steps. The plan thus becomes a verifiable artifact and execution becomes more stable. For verification, we also add a feedback-driven replanning mechanism. Under semantic-equivalence constraints, it identifies omissions and defects, rewrites and compresses the dependency matrix, and produces a more trustworthy final answer. Experiments on five logical-reasoning benchmarks and five LLMs show that, without relying on external solvers, MatrixCoT enhances both the robustness and interpretability of LLMs when tackling complex symbolic reasoning tasks, while maintaining competitive performance.
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。
