Really interesting concept -- instead of optimizing against static benchmarks or internally generated reward models, agents are evaluated through r…

iLands introduces an evaluation framework that replaces static benchmarks and internal reward models with real economic interactions. By using external market dynamics as a reward signal, the system aims to ground agent learning in objective, out-of-distribution feedback to mitigate reward hacking.