Insurance AI

…Scalable Oversight" -- arXiv:2605.28807 (William Overman et al.) This paper addresses what is increasingly the most tractable version of the scalable oversight problem: how should an AI agent behave when it operates in a context where the human overseer cannot reliably evaluate whether a specific action is beneficial? The paper introduces Calibrated Collective Oversight (CCO), which works by aggregating multiple auxiliary oversight…

…or are you at a point in the U-shaped curve where quantization tips you into the degradation regime? That is a more tractable engineering question than "did benchmark score go down?" and it suggests that models with high training SNR (shorter training on cleaner data) may be more robust to quantization than models with low SNR (over-trained on noisier data), even at the same parameter count. Why you should read it: ML…

…The reasoning model did not brute-force the problem through exhaustive search (which would be computationally intractable for this class of conjecture). Instead, it constructed a novel geometric arrangement that violated the conjecture's assumptions - the kind of creative insight that mathematicians characterize as "seeing something new." Whether this represents genuine mathematical creativity or a very sophisticated pattern…