We apply an internal robustness (iR) analysis to the recently released Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations Data Release 1 (DR1) dataset. This approach examines combinations of data subsets through a fully Bayesian model comparison, aiming to identify potential outliers, subsets possibly influenced by systematic errors, or hints of new physics. Using this approach, we statistically confirm the existence of three data points at $z=0.295$, 0.51, 0.71 as potential outliers. Excluding these points improves the internal robustness of the dataset by minimizing statistical anomalies and enables the recovery of $\mathrm{\Lambda }\mathrm{cold}$ dark matter ($\mathrm{\Lambda }\mathrm{CDM}$) predictions with a best-fit value of $w_0=-1.050\pm 0.128$ and $w_a=0.208\pm 0.546$. These results raise the intriguing question of whether the identified outliers signal the presence of systematics or point toward new physics.

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• Cosmological parameters
• Dark energy
• Large scale structure of the Universe