Who Takes the Lead? Model Ability and Task-Conditioned Collaboration with Humans
As language-model agents grow more capable, they can carry more of a task without asking anyone for help. Falling reliance on a human partner is easy to read as a failure of collaboration when it may instead be a correct division of labor, and the rate of human involvement alone cannot tell the two apart. We study this systematically by placing an agent and a person under a shared objective while varying who holds the advantage: the human has a clear advantage, neither does, or the agent does. To obtain this control, we introduce a shared-control maze, in which an agent-controlled and a human-controlled avatar share the objective of reaching a common target in as few team turns as possible.
| Authors | Weien Li, Haochen Liu, Zeyu Li et al. |
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| Published | 19 October 2026 |
| Last updated at source | — |
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