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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.
Achieving a 12.1% boost in robust concept removal, MapRoute++ redefines the boundaries of visual concept unlearning.
PolyAlign reveals that aligning models to context-specific human response distributions significantly enhances naturalness and faithfulness in multilingual interactions.
