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

• URL: http://arxiv.org/abs/2606.14502v1
• Date: Fri, 12 Jun 2026 14:33:55 GMT
• Title: From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI
• Abstract summary: Large Language Models (LLMs) are undergoing a fundamental transformation into integrated AI systems capable of reasoning, memory, and self-improvement.<n>We conceptualize this transition as a shift from episodic to Digital Colleague: from conversational answers to persistent work.
• Score: 74.72113315839412
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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