Kit Kyo

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.

A regime where the optimizer is not the bottleneck and cannot become one — how to recognise it, why every optimizer-side fix I tried did nothing, and why the one data-side intervention that appeared to work had to be withdrawn.

A three-act investigation into Safe Superintelligence Inc. that kept overturning itself: the ‘straight shot’ thesis is elegant but rests on an unverified premise; scaling didn’t die, it fragmented into four axes SSI can’t access; and the data flywheel’s reward signal is measurably broken — which turns SSI’s ‘no product’ from fatal weakness into audit qualification. Each act’s evidence almost closed the case; each layer of depth reopened it.

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.