This study investigates the systematic softening of potential energy surfaces (PES) in universal machine learning interatomic potentials (uMLIPs) and demonstrates that this issue can be effectively corrected through fine-tuning. The uMLIPs tested—M3GNet, CHGNet, and MACE-MP-0—exhibit a consistent underprediction of energies and forces in various atomic modeling tasks, including surface energies, defect energies, solid-solution energetics, phonon vibration modes, ion migration barriers, and high-energy states. This softening is attributed to biased sampling of near-equilibrium atomic configurations in the pre-training datasets, leading to systematic underprediction of PES curvature. The study shows that a single additional data point can significantly mitigate this issue through fine-tuning, with a simple linear correction derived from a single DFT reference label effectively reducing the softening effect. The results suggest that a significant portion of uMLIP errors are systematic and can be efficiently corrected with minimal data augmentation. This finding supports the data-efficient performance improvements observed in foundational MLIPs and highlights the importance of comprehensive materials datasets with improved PES sampling for future foundational MLIPs. The study also demonstrates that fine-tuning uMLIPs with a small amount of data can significantly reduce force MAE, with results showing that a single high-energy OOD configuration can effectively correct the softening issue. The results provide a theoretical foundation for the data-efficient performance boosts achieved by fine-tuning uMLIPs and emphasize the advantage of using large and comprehensive foundational AI models for atomic modeling. The study underscores the need for improved next-generation datasets to train foundational atomic models and further investigation into the role of model complexity in capturing the intricate details of the PES.This study investigates the systematic softening of potential energy surfaces (PES) in universal machine learning interatomic potentials (uMLIPs) and demonstrates that this issue can be effectively corrected through fine-tuning. The uMLIPs tested—M3GNet, CHGNet, and MACE-MP-0—exhibit a consistent underprediction of energies and forces in various atomic modeling tasks, including surface energies, defect energies, solid-solution energetics, phonon vibration modes, ion migration barriers, and high-energy states. This softening is attributed to biased sampling of near-equilibrium atomic configurations in the pre-training datasets, leading to systematic underprediction of PES curvature. The study shows that a single additional data point can significantly mitigate this issue through fine-tuning, with a simple linear correction derived from a single DFT reference label effectively reducing the softening effect. The results suggest that a significant portion of uMLIP errors are systematic and can be efficiently corrected with minimal data augmentation. This finding supports the data-efficient performance improvements observed in foundational MLIPs and highlights the importance of comprehensive materials datasets with improved PES sampling for future foundational MLIPs. The study also demonstrates that fine-tuning uMLIPs with a small amount of data can significantly reduce force MAE, with results showing that a single high-energy OOD configuration can effectively correct the softening issue. The results provide a theoretical foundation for the data-efficient performance boosts achieved by fine-tuning uMLIPs and emphasize the advantage of using large and comprehensive foundational AI models for atomic modeling. The study underscores the need for improved next-generation datasets to train foundational atomic models and further investigation into the role of model complexity in capturing the intricate details of the PES.