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Achieving reproducible evaluations across multiple modalities without the overhead of maintaining separate toolchains could revolutionize how foundation models are assessed.
Models can achieve similar accuracy while exhibiting starkly different reasoning failures, revealing a hidden complexity in AI performance that aggregate metrics overlook.
