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.

翻译：大型语言模型（LLMs）正经历从对话生成器向集成人工智能系统的根本性转变，这些系统具备推理、行动、记忆和自我改进能力。我们将这一转型概念化为从"聊天机器人"到"数字同事"的演进：即从对话式回答转向持续性工作。我们沿两个紧密耦合的维度组织这一转型。首先，在认知核心层面，LLMs正从基于下一词元预测的聊天机器人时代"快速思考"系统，演进为能利用推理时计算、思维链推理、反思、过程监督和强化学习的思维型LLM，以支持更审慎可靠的认知。其次，在工具增强型任务执行层面，LLMs正从临时调用外部资源的工具调用智能体，演进为配备持久工作空间、技能、验证循环和治理机制的"OpenClaw式工作站系统"。这种"工作空间+技能"范式通过状态持久化、可复用程序、任务封闭性和经验复用，将偶发性工具使用转变为类同事协作。我们考察了数据构建从指令-响应对向状态-行动-观测轨迹的转变，以及评估体系从静态基准向沙箱化、可审计、自我进化的人工智能生态系统的演进。