从聊天机器人到数字同事：向持久自主人工智能的范式转变
From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI
AI总结：
本文提出LLM从聊天机器人向数字同事的范式转变，通过认知核心（思考型LLM）和工具增强任务执行（OpenClaw工作站系统）两个维度，实现持久工作、状态持久化、可重用技能和自改进能力。
摘要：
大型语言模型（LLM）正在经历从对话生成器向集成AI系统的根本性转变，这些系统具备推理、行动、记忆和自我改进能力。我们将这一转变概念化为从聊天机器人到数字同事的转变：从对话式回答到持久工作。我们沿着两个紧密耦合的维度组织这一转变。首先，在认知核心层面，LLM正从聊天机器人时代由下一词预测驱动的“快速思考”系统，向思考型LLM发展，后者利用推理时计算、思维链推理、反思、过程监督和强化学习来支持更深思熟虑和可靠的认知。其次，在工具增强的任务执行层面，LLM正从临时调用外部资源的工具调用智能体，向配备持久工作空间、技能、验证循环和治理的OpenClaw式工作站系统（OpenClaw）发展。“工作空间+技能”范式通过状态持久化、可重用程序、任务闭合和经验复用，使偶发性的工具使用变得像同事一样。我们研究了数据构建从指令-响应对向状态-动作-观测轨迹的转变，以及评估从静态基准向沙盒化、可审计、自演进的AI生态系统的转变。
英文摘要：
Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-improvement. We conceptualize this transition as a shift from Chatbot to Digital Colleague: from conversational answers to persistent work. We organize this transition along two tightly coupled dimensions. First, at the cognitive core level, LLMs are advancing from Chatbot-era "fast thinking" systems driven by next-token prediction toward Thinking LLMs that leverage inference-time computation, Chain-of-Thought reasoning, reflection, process supervision, and reinforcement learning to support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing from tool-calling Agents that invoke external resources in an ad hoc manner toward OpenClaw-style workstation systems (OpenClaw) equipped with persistent Workspaces, skills, verification loops, and governance. The "Workspace + Skill" paradigm makes episodic tool use colleague-like via state persistence, reusable procedures, task closure, and experience reuse. We examine data construction shifts from instruction-response pairs to State-Action-Observation trajectories and evaluation from static benchmarks to sandboxed, auditable, self-evolving AI ecosystems.
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。
