Large Language Model agents face fundamental challenges in adapting to novel tasks due to limitations in tool availability and experience reuse. Existing approaches either rely on predefined tools with limited coverage or build tools from scratch without leveraging past experiences, leading to inefficient exploration and suboptimal performance. We introduce SMITH (Shared Memory Integrated Tool Hub), a unified cognitive architecture that seamlessly integrates dynamic tool creation with cross-task experience sharing through hierarchical memory organization. SMITH organizes agent memory into procedural, semantic, and episodic components, enabling systematic capability expansion while preserving successful execution patterns. Our approach formalizes tool creation as iterative code generation within controlled sandbox environments and experience sharing through episodic memory retrieval with semantic similarity matching. We further propose a curriculum learning strategy based on agent-ensemble difficulty re-estimation. Extensive experiments on the GAIA benchmark demonstrate SMITH's effectiveness, achieving 81.8% Pass@1 accuracy and outperforming state-of-the-art baselines including Alita (75.2%) and Memento (70.9%). Our work establishes a foundation for building truly adaptive agents that continuously evolve their capabilities through principled integration of tool creation and experience accumulation.

翻译：大型语言模型智能体因工具可用性与经验复用方面的局限，在适应新任务时面临根本性挑战。现有方法要么依赖覆盖范围有限的预定义工具，要么完全从头构建工具而未能利用过往经验，导致探索效率低下与性能欠优。本文提出SMITH（共享记忆集成工具枢纽），一种通过分层记忆组织将动态工具创建与跨任务经验共享无缝整合的统一认知架构。SMITH将智能体记忆组织为程序性、语义性与情景性三个组件，在保留成功执行模式的同时实现系统化能力扩展。我们的方法将工具创建形式化为受控沙箱环境内的迭代代码生成，并通过基于语义相似度匹配的情景记忆检索实现经验共享。此外，我们提出基于智能体集成难度重评估的课程学习策略。在GAIA基准上的大量实验验证了SMITH的有效性，其以81.8%的Pass@1准确率超越了包括Alita（75.2%）和Memento（70.9%）在内的最先进基线模型。本研究为构建真正自适应的智能体奠定了基础，使其能通过工具创建与经验积累的有机结合持续演进自身能力。