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Merging independently specialized checkpoints in parameter space beats joint multi-source continued pretraining for domain adaptation, retaining complementary domain signals that joint training tends to wash out.
Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
