Beyond Utility: When LLM Agents Start Dreaming Their Own Tasks
When large language models started solving math problems and writing code, they were celebrated as powerful tools. But a recent paper from INSAIT and ETH Zurich—LLM Agents Beyond Utility: An Open‑Ended Perspective—suggests something deeper may be stirring beneath the surface. The authors don’t simply ask what these agents can do, but whether they can want to do anything at all. From Obedience to Autonomy Most current LLM agents, even sophisticated ones like ReAct or Reflexion, live inside tight task loops: you prompt them, they plan, act, observe, and return a result. Their agency ends with the answer. But this study challenges that boundary by giving the agent a chance to set its own goals, persist across runs, and store memories of past interactions. ...