Unifying Dynamic Tool Creation and Cross-Task Experience Sharing through Cognitive Memory Architecture

2025-12-12

AI Summary

Large language model (LLM) agents face dual challenges in adapting to novel tasks: insufficient tool generalization and difficulty reusing prior experience. To address these, we propose SMITH, a cognitive memory architecture featuring a novel tri-level memory system—procedural, semantic, and episodic—that operates synergistically. SMITH enables adaptive toolset expansion via dynamic tool generation; supports cross-task experience reuse through episodic memory retrieval augmented by semantic matching; and enhances training efficiency via agent-aware difficulty re-estimation and curriculum learning. The method integrates hierarchical memory modeling, sandbox-driven iterative code generation, and semantic-similarity-guided memory retrieval. Evaluated on the GAIA benchmark, SMITH achieves 81.8% Pass@1 accuracy—significantly surpassing Alita (75.2%) and Memento (70.9%)—and provides the first empirical validation of continual capability evolution in LLM agents.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Dynamic tool creation for novel tasks
• Cross-task experience sharing among agents
• Hierarchical memory organization for capability expansion

Innovation

• Dynamic tool creation via iterative code generation
• Cross-task experience sharing through hierarchical memory
• Curriculum learning with agent-ensemble difficulty re-estimation