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Transformers encode an inferred partner's expertise early in the network, but interventions at peak decodability produce virtually zero downstream effect until patched past the midpoint.
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.
