A recurrent-depth language model ships six ways to stop early. Measuring one of them at its own default: it selects better stopping points than chance, and on one task it still loses accuracy on the answers. Both are true, and separating them needed two different experiments.
read more →We put interpretable, locally-computable factors from four mechanistically-different domains — software-defect prediction, paper acceptance, AI-text detection, and a world-model physics proxy — on one shared per-factor scorecard. Predictive power has a domain-dependent ceiling, the factors don’t transfer across domains, and a single-factor reading nearly had us call an industrial benchmark a ‘collapse’ when the signal was just multivariate.
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