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Molecular generation models can now achieve 100% validity and state-of-the-art distributional alignment by serializing higher-order topology into context-free grammar rules.
Multimodal models can achieve significant image generation improvements by learning from their own critiques during test-time compute, effectively acting as their own recursive self-improvement mechanism.
TabPFN-Rel not only tops the leaderboard on RelArena-$\alpha$ but also challenges the notion that specialized architectures are always superior to flattened relational databases.
