Measuring LLM performance drift: observations and methodology from 31,352 repeated benchmark measurements [D]

This story is from 2026-09-07. It is preserved in the archive; the latest stories are on the live feed.

One thing that has bothered me about LLM benchmarks for a while is that most of them are essentially snapshots. A model is evaluated, a score is published, and we tend to talk about that score as if it describes a relatively stable object. But with API-served models, the thing behind the model name…

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1. 2026-09-07 07:44 · r/MachineLearning
   Measuring LLM performance drift: observations and methodology from 31,352 repeated benchmark measurements [D]