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Discussion about the data sources labs may use to train 10T parameter models, including synthetic reasoning chains and human-generated traces, amid concerns about hitting the data wall.
This paper empirically characterizes uncertainty in thinking-mode visual language models, demonstrating that the thinking chain entropy is a more reliable signal for hallucination detection than conventional answer token distribution, which collapses in these models.