对话智能体的一个期望目标：能力、挑战与未来方向
A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions
AI总结：
本综述提出对话智能体的期望目标，从推理、监控、控制三维度分析LLM驱动智能体，指出评估、长期推理、自我进化等关键研究方向，推动迈向AGI。
摘要：
大型语言模型（LLMs）的最新进展已将对话式人工智能从传统对话系统推进到能够自主行动、具备上下文感知能力并与用户进行多轮交互的复杂智能体。然而，关于其能力、局限性和前进路径的基本问题仍然悬而未决。本综述论文提出了下一代对话智能体的一个期望目标——已取得的成就、依然存在的挑战，以及为实现更接近人类智能水平的可扩展系统所必须采取的措施。为此，我们通过将LLM驱动的对话智能体的能力组织为三个主要维度来系统性地分析它们：（i）推理——受人类智能启发的、用于决策的逻辑性系统性思考；（ii）监控——涵盖自我意识和用户交互监控；（iii）控制——聚焦于工具利用和策略遵循。在此基础上，我们引入了一种新的分类法，围绕我们提出的期望目标对近期对话智能体的研究工作进行了分类。我们识别了关键的研究空白，并概述了关键方向，包括现实评估、长期多轮推理能力、自我进化能力、协作与多智能体任务完成、个性化以及主动性。这项工作旨在提供一个结构化的基础，突出现有局限性，并为对话智能体潜在的未来研究方向提供见解，最终推动向通用人工智能（AGI）的进步。我们维护了一个精选的论文仓库，网址为：https://github.com/emrecanacikgoz/awesome-conversational-agents。
英文摘要：
Recent advances in Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. Yet, fundamental questions about their capabilities, limitations, and paths forward remain open. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence. To that end, we systematically analyze LLM-driven Conversational Agents by organizing their capabilities into three primary dimensions: (i) Reasoning - logical, systematic thinking inspired by human intelligence for decision making, (ii) Monitor - encompassing self-awareness and user interaction monitoring, and (iii) Control - focusing on tool utilization and policy following. Building upon this, we introduce a novel taxonomy by classifying recent work on Conversational Agents around our proposed desideratum. We identify critical research gaps and outline key directions, including realistic evaluations, long-term multi-turn reasoning skills, self-evolution capabilities, collaborative and multi-agent task completion, personalization, and proactivity. This work aims to provide a structured foundation, highlight existing limitations, and offer insights into potential future research directions for Conversational Agents, ultimately advancing progress toward Artificial General Intelligence (AGI). We maintain a curated repository of papers at: https://github.com/emrecanacikgoz/awesome-conversational-agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
