eMoT: 通过符号锚定和记忆腐蚀演化的思维记忆
eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion
AI总结：
提出eMoT框架，通过记忆腐蚀、符号锚定和一致性精炼三个模块，将推理轨迹视为动态演化记忆，以稳定多步推理并提升准确率与一致性。
摘要：
尽管大型语言模型（LLMs）在多步推理任务上取得了令人印象深刻的性能，但其可靠性仍然受到关键限制的阻碍，例如不受约束的幻觉和较差的数值计算。从根本上说，这些问题源于标准模型将推理视为一次性的瞬态生成过程，而不是保留并改进成功的程序逻辑。为了解决这些挑战，我们提出了eMoT（演化的思维记忆），这是一个统一框架，通过将推理轨迹视为动态演化的记忆而非静态模板来稳定多步推理。该框架主要由三个相互连接的模块组成：（i）记忆腐蚀机制，强化高效用推理结构，同时逐渐衰减较少使用的结构；（ii）符号锚定引擎，利用Python进行确定性计算，类似于人类使用计算器；（iii）一致性驱动的精炼过程，将神经推理与符号结果对齐，减少逻辑差异的累积。在多个推理基准上，eMoT相比标准的思维链和结构化推理基线提高了准确率和解决方案一致性。在传统任务Game of 24上，eMoT达到了100%的准确率，比基线高出17.6%。在数学任务GSM8K、ASDiv、SVAMP和MGSM上的评估进一步显示了在多步数学推理中的持续改进。在我们的评估中，尽管使用了轻量级骨干模型且基线能力受限，我们仍取得了优越的性能。与依赖大规模模型的替代方法相比，我们的结果表明性能提升根本上是由eMoT框架的推理控制驱动的，而非单纯的模型规模。
英文摘要：
While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical limitations such as unconstrained hallucinations and poor numerical computation. Fundamentally, these issues arise because standard models treat reasoning as a transient, one-off generation process rather than retaining and refining successful procedural logic. To address these challenges, we propose eMoT (evolving Memory-of-Thought), a unified framework that stabilizes multi-step reasoning by treating reasoning trajectories as dynamic, evolving memories rather than static templates. The framework primarily consists of three interconnected modules: (i) a memory corrosion mechanism that reinforces high-utility reasoning structures while gradually decaying less frequent ones; (ii) a symbolic anchoring engine that utilizes Python for deterministic computation, much like a human uses a calculator; and (iii) a consistency-driven refinement process that aligns neural inference with symbolic outcomes, reducing the accumulation of logical discrepancies. Across multiple reasoning benchmarks, eMoT improves accuracy and solution consistency over standard Chain-of-Thought and structured reasoning baselines.On the traditional task Game of 24, eMoT achieves 100% accuracy, surpassing the baseline by up to 17.6%. Evaluations on mathematical task GSM8K, ASDiv, SVAMP, and MGSM further show consistent gains in multi-step mathematical reasoning. In our evaluation, we achieve superior performance despite utilizing a lightweight backbone model with constrained baseline capabilities. Compared to alternative methods that rely on massively scaled models, our results demonstrate that the performance gains are fundamentally driven by the eMoT framework's reasoning control rather than sheer model size.
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。
