Autonomy Is Absent, Not Eroded: An Observational Study of User Autonomy in Multi-Turn LLM Conversations

arXiv:2611.09923v1 Announce Type: cross Abstract: Does prolonged interaction with Large Language Models reduce users' observable autonomy during conversation? We test this degradation hypothesis by scoring user autonomy across 2,877 multi-turn conversations (46,952 turns) from WildChat and validating on 500 conversations from LMSYS-Chat-1M. Our core finding is a null result: population-level autonomy remains stable throughout conversations, only 4.7% of conversations show meaningful decline, and an apparent decrease at later turn positions is explained by survivorship bias. Instead, the dominant pattern is not erosion but absence: 80.8% of conversations exhibit zero or low autonomy throughout. When autonomy engagement occurs, it is self-reinforcing (persistence doubles from 40% to 85% over consecutive turns). These findings reframe the problem: the risk is not that LLMs progressively degrade users' observable autonomy, but that most users never engage in independent reasoning during the interaction.