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Ditch KL divergence for IPMs in Bayesian experimental design and watch your credible sets tighten and your designs stabilize, even when your model's a bit off.
Forget hand-crafting prototypes for interpretable RL: this method learns them directly from the data, matching the performance of expert-designed systems.
Forget hand-tuning PDE solvers – a new multi-agent framework designs, implements, debugs, and verifies them directly from natural language, outperforming neural and LLM baselines.
