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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.
Even state-of-the-art multimodal models struggle with reliability in clinical tool use, revealing critical gaps in AI agent performance.
You don't need massive datasets to build effective remote sensing foundation models; FLORO achieves competitive performance with models trained on 100x more data.