LLMs Evolve from Chatbots to Persistent Digital Colleagues

This paper outlines a significant paradigm shift in the development of large language models (LLMs), moving them beyond their current role as conversational chatbots towards becoming persistent, autonomous "Digital Colleagues." This evolution is characterized by LLMs gaining enhanced capabilities in reasoning, action, memory, and self-improvement, enabling them to engage in more deliberate and sustained work. The transformation is conceptualized along two main dimensions. Firstly, at the cognitive core, LLMs are evolving into "Thinking LLMs" that utilize advanced inference-time computation, Chain-of-Thought reasoning, reflection, and reinforcement learning to achieve more reliable and deliberate cognition. This moves them beyond simple next-token prediction. Secondly, at the task execution level, LLMs are progressing from ad-hoc tool-calling agents to "OpenClaw-style workstation systems." These systems are designed with persistent Workspaces, reusable skills, verification loops, and governance mechanisms. This "Workspace + Skill" paradigm allows for state persistence, task closure, and experience reuse, making tool use more integrated and colleague-like. The paper also discusses the shift in data construction and evaluation methods required for these evolving AI ecosystems.