让AI像人一样思考：多智能体协作+记忆复用，提升表格问答准确率
MAPLE: Multi-Agent Adaptive Planning with Long-Term Memory for Table Reasoning
- 用四个专业智能体构成反馈循环，模拟人类解题过程
- 在WiKiTQ和TabFact上超越现有方法，多模型表现最佳
- 适合需要复杂推理的表格问答任务，尤其关注可解释性
基于表格的问题回答需要复杂的推理能力，当前大语言模型在单次推理中难以实现。现有方法如思维链和问题分解缺乏错误检测机制，且会丢弃解题经验，与人类解题方式差异显著。本文提出MAPLE（多智能体自适应规划与长期记忆框架），通过专门的认知智能体在反馈驱动循环中协作，模拟人类问题解决过程。MAPLE集成四大组件：(1) 使用ReAct范式的求解器进行推理，(2) 验证答案的检查器，(3) 诊断错误并修正策略的反思者，(4) 管理长期记忆以实现经验复用与进化。在WiKiTQ和TabFact上的实验表明，该框架在多个LLM骨干模型上均取得显著提升，达到当前最优性能。
原文摘要 · Abstract (English)
Table-based question answering requires complex reasoning capabilities that current LLMs struggle to achieve with single-pass inference. Existing approaches, such as Chain-of-Thought reasoning and question decomposition, lack error detection mechanisms and discard problem-solving experiences, contrasting sharply with how humans tackle such problems. In this paper, we propose MAPLE (Multi-agent Adaptive Planning with Long-term mEmory), a novel framework that mimics human problem-solving through specialized cognitive agents working in a feedback-driven loop. MAPLE integrates 4 key components: (1) a Solver using the ReAct paradigm for reasoning, (2) a Checker for answer verification, (3) a Reflector for error diagnosis and strategy correction, and (4) an Archiver managing long-term memory for experience reuse and evolution. Experiments on WiKiTQ and TabFact demonstrate significant improvements over existing methods, achieving state-of-the-art performance across multiple LLM backbones.
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