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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.
This work addresses the adverse interactions between sparsity and data repetition, and presents evidence for the core mechanisms of overfitting and its potential remediation, and suggests promising avenues for future methods to reduce over-specialization in model parameters by disrupting memorization patterns.
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.
SurfBind's innovative surface-centric approach outperforms traditional methods, revealing the critical role of molecular surface interactions in epitope prediction.
M* achieves up to 2.9x lower real-time factor and 2.7x higher throughput for text-to-speech tasks, revolutionizing how we serve complex multimodal AI models.
