# 从聊天机器人到数字同事：持久自主AI的范式转变

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-12 08:00
- AIHOT 分数：35
- AIHOT 链接：https://aihot.virxact.com/items/cmqemsqub03pnsluno9ciq7ul
- 原文链接：https://arxiv.org/abs/2606.14502

## AI 摘要

大语言模型正从对话生成器转向集成推理、行动、记忆与自我改进的AI系统。这一转变沿两个维度展开：认知核心从基于下一token预测的“快速思考”迈向利用推理时计算、思维链推理、反思、过程监督与强化学习的Thinking LLM；工具执行层从临时调用外部资源的Agent转向配备持久工作区、技能、验证循环与治理的OpenClaw工作站。“工作区+技能”范式通过状态持久化与经验复用实现持续协作。数据构建从指令-响应对转向状态-动作-观察轨迹，评估从静态基准转向沙盒化、可审计、自我进化的生态系统。

## 正文

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.