# Robust Trust

arXiv: https://arxiv.org/abs/2602.09490 · HTML: https://mlsanity.com/paper/2602.09490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 1 of 1)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)

## Built on by (most cited first, 1 of 1)

- [Mechanism Design for Alignment and Control](https://mlsanity.com/paper/2609.01595/) (2026)

## Abstract (arXiv)

An agent chooses an action based on her private information and a recommendation from an informed but potentially misaligned adviser. With a known probability, the adviser truthfully reports his signal; with the remaining probability, he can send any message. We characterize optimal robust decision rules that maximize the agent's worst-case expected payoff. Every optimal rule is equivalent to a trust-region policy in belief space: the adviser's reported beliefs are taken at face value if they fall within the trust region but are otherwise clipped to the trust region's boundary. We derive alignment thresholds above which advice is strictly valuable and fully characterize the solution in both binary-state and binary-action environments.