Co-π-tree shows that LLM reasoning can be distilled into executable and interpretable policy trees for human-AI collaboration. The learned policy tree executes directly at test time, reduces online LLM dependence, and supports local branch-level inspection and refinement.
Interpretability
Partner prediction and action selection remain explicit in the policy tree.
Efficiency
After learning, execution proceeds without repeated LLM calls at test time.
Collaboration
Reasoning about a partner provides a useful signal for coordination with AI and human partners.