The article analyzes why autonomous LLM agents become expensive, breaks down their cost components, and presents concrete engineering strategies—memory management, tool‑call optimization, and planning constraints—to dramatically reduce token usage and improve reliability while maintaining performance.
A joint survey by nine leading Chinese institutions outlines the efficiency crisis of modern AI agents and proposes three strategic directions—efficient memory, tool learning, and planning—detailing concrete mechanisms, representative models, and emerging trends for building high‑performing, low‑cost agents.
Tool learning enables foundation models to follow human instructions and use external tools, and this article reviews Tsinghua’s research, introduces the WebCPM interactive web‑search QA framework, BMTools and ToolBench packages, and discusses background, frameworks, applications, and future challenges in AI tool usage.
This article reviews the emerging field of tool learning for large foundation models, outlining its background, categorization, core framework components, training strategies, and applications such as WebCPM, BMTools, and ToolBench, while highlighting recent research results and open‑source resources.
