OpenAI describes GPT-5 Pro as using "scaled but efficient parallel test-time compute." Nathan Lambert on Lex Fridman #490 discusses the broader pattern of inference-time scaling: giving models more compute at generation time to explore multiple reasoning paths. ↩
Фото: Владимир Солнцев / ТАСС
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Another way to approach dithering is to analyse the input image in order to make informed decisions about how best to perturb pixel values prior to quantisation. Error-diffusion dithering does this by sequentially taking the quantisation error for the current pixel (the difference between the input value and the quantised value) and distributing it to surrounding pixels in variable proportions according to a diffusion kernel . The result is that input pixel values are perturbed just enough to compensate for the error introduced by previous pixels.
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Percentile 90: 340.282 ms | 110.817 ms
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