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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.
Achieving state-of-the-art TSC performance without real data, TimEE redefines the potential of synthetic pre-training in classification tasks.
AgentODE reveals that even with limited data, mechanistic insights can be extracted from population-level statistics, challenging the reliance on individual-level data in rare disease modeling.
Existing tabular foundation models excel only on small IID datasets, leaving a significant gap in performance on more complex, real-world data challenges.
