Unverified50% confidenceFactExact time
Emergent outcomes are often difficult to reproduce and highly sensitive to initial conditions, prompts, and random seeds.
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50%
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Long-term
Relevance
10/30/2026
First Seen
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Related Claims
UnverifiedSetting a random seed can only guarantee consistency in initialization, data order, and explicit random sampling; it cannot eliminate non-determinism caused by floating-point parallel computation during backpropagation.72% similarUnverifiedThe paper points out that an acceptance mechanism tolerant of the existence of random tapes may accept incorrect outputs.72% similarUnverifiedLosing the prefill checkpoint at the end of the prompt under sparse sampling conditions can cause state corruption, output deviation, and even non-reproducible generation results.72% similarUnverifiedEven with models that have undergone abliteration (uncensoring), directly generating random prompts tends to yield monotonous and narrow results.71% similarUnverifiedThe results of a single-task evaluation are subject to randomness, and the same model may produce noticeably different answers under different prompts and different random seeds.71% similar
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