让AI推理过程可复用，减少重复计算，提升效率。
Graph-Memoized Reasoning: Foundations Structured Workflow Reuse in Intelligent Systems
- 将推理步骤存为图结构记忆，支持按相似性检索复用
- 通过优化目标平衡计算成本与推理一致性，降低冗余
- 适合需要持续学习和高效推理的智能系统开发者
基于大语言模型的推理系统在不同任务间频繁重复计算相似推理步骤，造成资源浪费、延迟增加并影响可复现性。为此，我们提出图记忆化推理（Graph-Memoized Reasoning），一种将推理工作流作为图结构记忆进行表示、存储与重用的形式化框架。通过编码过往决策图，并基于结构与语义相似性检索，该方法实现子图在新任务中的组合式复用。我们构建了一个优化目标，以最小化总推理成本并正则化存储与生成工作流之间的不一致，为智能系统中的效率-一致性权衡提供理论基础。同时，我们设计了与该优化目标对齐的概念评估协议。本框架为可解释、低成本、自我改进的推理架构奠定基础，推动大规模代理系统中持久记忆的发展。
原文摘要 · Abstract (English)
Modern large language model-based reasoning systems frequently recompute similar reasoning steps across tasks, wasting computational resources, inflating inference latency, and limiting reproducibility. These inefficiencies underscore the need for persistent reasoning mechanisms that can recall and reuse prior computational traces. We introduce Graph-Memoized Reasoning, a formal framework for representing, storing, and reusing reasoning workflows as graph-structured memory. By encoding past decision graphs and retrieving them through structural and semantic similarity, our approach enables compositional reuse of subgraphs across new reasoning tasks. We formulate an optimization objective that minimizes total reasoning cost regularized by inconsistency between stored and generated workflows, providing a theoretical foundation for efficiency-consistency trade-offs in intelligent systems. We outline a conceptual evaluation protocol aligned with the proposed optimization objective. This framework establishes the groundwork for interpretable, cost-efficient, and self-improving reasoning architectures, offering a step toward persistent memory in large-scale agentic systems.
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