Fix the Measurement Model, Not the Acquisition Rule: Optimiser Repairs for Living-Cell Self-Driving Laboratories

Closed-loop Bayesian optimisation assumes things that living cells do not provide. A measured cell is used up and cannot be measured again, cultures drift over a campaign, day-to-day batch offsets shift every reading, and cell-to-cell variation is heavy-tailed and mixed with probe failures. When an autonomous loop goes wrong under these conditions, the usual response is a selectionlevel repair: change the incumbent, replicate the current best, or explore more. The other option is to change what the model assumes about each measurement. We compared the two in a simulated living-cell laboratory with common random numbers, crossing two surrogate models with four selection repairs over three response surfaces, four pathology regimes and 60 seeds (8,820 campaigns of 30 rounds, five destroyed cells per round). A measurement-aware model, using a robust per-condition summary, shrunk per-condition noise and additive drift and day terms, lowered final simple regret against plain GP-EI by 0.042 [0.024, 0.061] under heavy tails and by 0.039 [0.013, 0.064] with all pathologies on, and raised 90% band coverage under drift from 0.83 to 0.95. It cost 0.015 [0.004, 0.027] on clean data. No selection repair helped either model once the data were contaminated. The noisy-EI incumbent, a standard fix for noisy optimisation, raised regret in every regime for both models (+0.015 to +0.045). For agents that run living-cell loops, the lever is the measurement model, and the acquisition rule is best left alone.