YRC-Bench: A Benchmark for Learning to Coordinate with Experts

AI agents need to know their limits: The paper addresses a critical safety challenge—AI systems must recognize when they lack the capability to handle a situation and defer to human experts or specialized systems rather than attempting tasks beyond their competence.

Expert consultation requires strategic decision-making: Since expert assistance is costly (in terms of time, resources, or computational expense), the key challenge is determining when to ask for help. The paper frames this as learning to coordinate with experts efficiently rather than relying on them constantly.

Training without expert interaction is feasible: The YRC-0 problem demonstrates that agents can learn to work effectively with experts in new situations without having practiced with those experts during training. This "unsupervised" approach to expert collaboration could reduce the cost of developing safe AI systems.

Standardized benchmarks advance responsible AI research: By introducing YRC-Bench with diverse environments, a common evaluation framework, and baseline implementations, the authors provide infrastructure that enables the broader research community to develop and compare methods for building expert-leveraging agents—similar to how standardized benchmarks have accelerated progress in other AI safety areas.

Decomposing the problem reveals insights: The proposer-validator framework used in the paper helps diagnose which components of expert-coordination systems work well and which need improvement, offering a structured approach to understanding and improving AI safety mechanisms.