We consider the problem of a mediator learning to coordinate a group of strategic agents via recommendations of actions to take without knowledge of their underlying utility functions (e.g.

However, the inferred reward function often fails to capture the underlying task objective.

Unfortunately, existing faithfulness criteria for PRP are limited to coarse-grained LLM-based scoring without a clear definition or formulation.

Our code is available at: https://github.com/LINs-lab/ReLA .

In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.