通过人机协同设计，让智能体规划更高效。
Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface
- 将用户打断融入系统设计，实现人机协同规划。
- 利用人类介入提供中间步骤，显著降低规划延迟。
- 适合注重交互效率的智能体应用开发者。
智能体作为以用户为中心的工具，正被广泛用于任务委托，通过生成思考、与用户代理互动及制定行动方案来响应各类请求。然而，基于大语言模型（LLMs）的智能体常因模型本身规模庞大、资源需求高，以及智能体结构复杂（需生成大量中间思考）而面临显著的规划延迟。服务效率低下会削弱自动化的价值。本文提出一种以人为中心的高效智能体规划方法——交互式推测规划（Interactive Speculative Planning），旨在通过系统设计与人机界面的协同优化，提升智能体规划效率。该方法强调将用户交互与中断视为系统核心组成部分，使智能体能流畅应对用户干预。通过引入人-机协同机制，利用人类在循环中的实时反馈提供准确的中间步骤，从而加速整体流程。代码与数据将公开发布。
原文摘要 · Abstract (English)
Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language models (LLMs) often face substantial planning latency due to two primary factors: the efficiency limitations of the underlying LLMs due to their large size and high demand, and the structural complexity of the agents due to the extensive generation of intermediate thoughts to produce the final output. Given that inefficiency in service provision can undermine the value of automation for users, this paper presents a human-centered efficient agent planning method -- Interactive Speculative Planning -- aiming at enhancing the efficiency of agent planning through both system design and human-AI interaction. Our approach advocates for the co-design of the agent system and user interface, underscoring the importance of an agent system that can fluidly manage user interactions and interruptions. By integrating human interruptions as a fundamental component of the system, we not only make it more user-centric but also expedite the entire process by leveraging human-in-the-loop interactions to provide accurate intermediate steps. Code and data will be released.
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