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School of Artificial Intelligence, Laboratory for Novel Software Technology, Nanjing University National Key, Nanjing University
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Later Transformer layers in tabular foundation models aren't always the smartest; revisiting earlier thoughts dramatically boosts performance.
Treating all latent tokens identically bottlenecks multimodal test-time compute鈥攄isentangling hidden-state updates into visual-grounding and high-entropy reasoning channels beats standard output-space search under matched sample budgets.
CausalArena is introduced, a unified and evolvable benchmark for causal discovery under a common protocol, and substantial ranking shifts across SCM families and protocols are revealed, showing that strong performance in one benchmark regime does not reliably transfer to others.
