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Common approaches to addressing VLM inter-run drift include fixing the random seed, setting the temperature to 0, or sampling the same page multiple times and taking a majority vote.
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10/11/2026
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UnverifiedStrategies for reducing randomness in AI model outputs include: setting a lower temperature value, requiring confidence scores in the output, a multi-inference voting mechanism, and retaining a human review step.65% similarUnverified通过记录随机种子可精确复现导致失败的特定顺序，再通过二分法逐步缩小污染源的范围63% similarVerifiedLarge model outputs are stochastic (controlled by the Temperature parameter), so a single comparison is not conclusive and requires multiple sampling and statistical comparisons.62% similarVerifiedEven when Temperature is set to 0 (greedy decoding), the order of parallel floating-point operations on modern GPUs can still lead to minor differences in results.62% similarVerifiedThe non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.61% similar
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