arXiv AI September 3, 2026 By Yifan Zhu, Sammie Katt, Samuel Kaski

Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality

The paper introduces ProSE, a framework for AI assistants that generate proposals while considering users’ bounded rationality and evaluability constraints. It proposes a KL‑regularised bounded‑rational binary response model and a depth‑2 Bayes‑adaptive planner, “ProSE‑Plan,” which scores proposals by expected responses and resulting belief updates. Experiments on graph simulations show that “ProSE‑Plan” outperforms evaluability‑unaware and myopic baselines, especially when evaluation cost is high, and that informative probes are crucial for effective assistance.

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