呼唤协同智能：为何人机协作系统应优先于AI自主性

摘要

近期大型语言模型（LLMs）的进步促使许多研究者致力于构建完全自主的AI代理。本立场文件质疑这一方向是否恰当，因为这类自主系统在可靠性、透明度及理解人类实际需求方面仍存在问题。我们提出一种不同的路径：基于LLM的人机协作系统（LLM-HAS），其中AI与人类协作而非取代人类。通过保持人类的参与，提供指导、解答疑问并维持控制，这些系统能更具可信度与适应性。通过审视医疗、金融及软件开发领域的实例，我们展示了人机协作如何比AI单独工作更有效地处理复杂任务。同时，我们探讨了构建此类协作系统的挑战，并提供了实用解决方案。本文主张，AI的进步不应以系统的独立性为衡量标准，而应以其与人类协作的效能为准绳。AI最有前景的未来不在于接管人类角色的系统，而在于通过有意义的伙伴关系增强人类能力的系统。

English

Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership.

呼唤协同智能：为何人机协作系统应优先于AI自主性

A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

摘要

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