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Fixed-weight Transformers can perfectly replicate iterative causal discovery algorithms, revealing a critical distinction between executing an algorithm and recovering the true causal structure.
Muon's finite-step orthogonalization provably guarantees reaching any target training loss for wide networks, resolving a key theoretical gap.
