itself. >> Yeah. So, if you look at like let's say let's take an easy example. Let's take a science or like scientific discovery as an example, right? Usually these are one-shot problems. It's incredibly hard. You have models think through this process, right? So, in that you have tons of sub-domains. Like tons and tons and tons. So, in that aspect, if you think about post-training like the post-training process of a model, they'd be tuned with different teachers. They'd be tuned on different sub-tasks. So, those those um those overlapping strengths will be readily apparent when you're trying to understand failures of each models on different different sub-tasks. Once you understand that, you can orchestrate your system to leverage that arbitrage essentially, and that essentially becomes free. So, I think this is on LM router bench. There was there are tons of benchmarks out there. But, if you use these techniques, you can get like up to 10% higher accuracy even, right? It depends on the model pool. Depends on the task at hand. But, I would encourage to think about the complementary nature of models.