AI 从聊天机器人变成数字同事
你还在把 AI 当聊天框里的问答机？这篇论文说，大模型正在从“聊天机器人”变成“数字同事”——一个能记住上下文、调用工具、自我改进的持久工作伙伴。具体怎么变？两条路：一是让模型学会“慢思考”，不再只靠猜下一个词，而是用推理链、反思、强化学习来更可靠地解决问题；二是给它一个“工作台+技能库”，让它能像人一样保存状态、复用流程、验证结果，而不是每次临时调用工具。这意味着未来的 AI 能持续跟进你的项目，而不是每次对话都从零开始。它不是你明天就能用的功能，但指明了 AI 从玩具变成工具的方向。
📄 原文摘要(英文)
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.