Unverified50% confidenceOpinionExact time
The core difficulty of approximating deterministic logic with neural networks is that deterministic logic requires step-by-step progression with zero errors, whereas neural networks are inherently continuous and approximate and susceptible to the accumulation of numerical errors, so small errors are amplified during long-sequence execution.
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10/30/2026
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