The Year in AI Papers
The year/Independent research

Paper 2606.14502

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

Yongheng Zhang, Ziang Liu, Jiaxuan Zhu, Shuai Wang, Xiangqi Chen, Haojing Huang, Jiayi Kuang, Siyu Chen, Ao Shen, Hao Wu, Qiufeng Wang, Qian-Wen Zhang, et al.

Published
Jun 2026
Research lab
Independent
Citations
3
GitHub
Not linked

01 In brief

Summary

This survey paper by Tencent Youtu Lab and academic partners frames the evolution of Large Language Models (LLMs) as a shift from conversational chatbots to persistent, autonomous 'Digital Colleagues.' The authors organize this transition along two dimensions: the cognitive core (from fast, next-token-prediction 'Chatbot' models to 'Thinking LLMs' that use inference-time computation, Chain-of-Thought, and reinforcement learning) and tool-augmented task execution (from ad-hoc tool-calling 'Agents' to 'OpenClaw-style' workstation systems with persistent workspaces, reusable skills, and governance).

The central thesis is that the 'Workspace + Skill' paradigm enables durable, colleague-like work through state persistence, reusable procedures, and task closure.

The paper also examines paradigm shifts in data (from instruction-response pairs to state-action-observation trajectories) and evaluation (from static benchmarks to task-closure and safety metrics).

It concludes by discussing open challenges in reliability, safety, and governance, and outlines future directions toward self-evolving AI ecosystems.

02 From the paper

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.