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The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.
Hallucinations in multimodal reasoning models are linked to high-entropy transition words, and can be reduced by decoding with probability-weighted continuous embeddings rather than discrete tokens during these uncertain states.
