From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

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large-language-models ai-agents reasoning tool-use persistent-ai autonomous-systems

Summary

This paper conceptualizes the transition of large language models from conversational chatbots to persistent autonomous AI colleagues, focusing on improved reasoning and tool-augmented task execution with workspace and skill paradigms.

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.
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Abstract

Large Language Models are evolving from conversational systems to integrated AI colleagues with enhanced reasoning capabilities and persistent work environments.

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 fromChatbottoDigital 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 fromChatbot-era “fast thinking” systems driven by next-token prediction towardThinking LLMsthat leverageinference-time computation,Chain-of-Thought reasoning,reflection,process supervision, andreinforcement learningto support more deliberate and reliable cognition. Second, at the tool-augmented task execution level, LLMs are progressing fromtool-calling Agentsthat invoke external resources in an ad hoc manner towardOpenClaw-style workstation systems (OpenClaw) equipped with persistentWorkspaces,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 toState-Action-Observation trajectoriesand evaluation from static benchmarks to sandboxed, auditable,self-evolving AI ecosystems.

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