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Open-weight embedding models fine-tuned via contrastive ranking beat frontier LLMs like GPT-5.2 and Claude-Sonnet-4.6 by over 10 percentage points on real-world entity disambiguation.
Role-linked visual biases in text-to-image models persist and even intensify when placed in unrelated contexts, challenging assumptions about context-free evaluations.
