## 6 Conclusions
Imputation strategies should be tailored to task-specific sensitivity, leveraging the identified boundary conditions between beneficial and harmful imputation. For noise-sensitive tasks, such as reinforcement learning, conservative imputation approaches and stricter data-quality controls are essential to maintain model stability and performance. Practitioners should prioritize accurate data collection and preprocessing for the critical subset identified as having the most significant impact on performance. Specifically, in scenarios with limited resources, efforts should focus on minimizing noise in key data elements rather than indiscriminately expanding the dataset. **Future Work**  
Although this study evaluates two distinct tasks, the generalizability of its findings to other domains remains uncertain. The current study employs straightforward corruption methods, whereas real-world data corruption can be more complex, involving correlated or structured patterns of corruption not accounted for in the current experiments. Additionally, the artificial method of controlling imputation accuracy by inserting random noise provides useful insights into theoretical trade-offs but may not fully capture the nuances of practical imputation techniques. Moreover, although standard imputation methods were investigated, more advanced and sophisticated approaches remain underexplored, leaving their potential effectiveness unclear. This study opens up several avenues for future research. First, the observed patterns and empirical rules should be validated across broader datasets and additional machine learning tasks, such as computer vision and time-series forecasting. While the core principles-exponential performance decay, imputation decision boundaries--are hypothesized to generalize, their manifestations will likely depend on domain-specific factors. For instance, spatial correlations in CV or temporal dependencies in time-series data may influence how corruption impacts model performance. Notably, CV tasks may exhibit greater noise resilience compared to NLP, as pixel-level redundancies in images can mitigate localized corruption, whereas natural language is heavily compressed. Second, developing adaptive imputation strategies that dynamically balance missing data recovery and noise introduction could further enhance model robustness. Furthermore, numerous techniques have been developed for error detection, removal, and correction [30], investigating the effectiveness of these methods presents an interesting direction for future study. Finally, theoretical work on the relationship between information entropy, marginal utility, and model learning dynamics under corrupted data could deepen our understanding of these phenomena. By addressing these challenges, the hope is to advance the field's ability to build robust machine learning models that perform reliably even in the presence of real-world data corruption. Conceptualization, Q.L. and W.M. ; methodology, Q.L. ; software, Q.L. ; validation, Q.L. and W.M. ; formal analysis, Q.L. ; investigation, Q.L. and W.M. ; resources, W.M. ; writing--original draft preparation, Q.L. ; writing--review and editing, Q.L. and W.M. ; visualization, Q.L. ; supervision, W.M. ; project administration, W.M. ; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Shanghai Baiyulan Talent Project Pujiang Program grant number 24 PJD115, Shanghai Yangpu District Postdoctoral Innovation & Practice Base Project. The data presented in this study are openly available in [data-corruption-study] at [[[https://github.com/qiliuchn/data-corruption-study](https://github.com/qiliuchn/data-corruption-study)]([https://github.com/qiliuchn/data-corruption-study](https://github.com/qiliuchn/data-corruption-study)) (accessed on 20 May 2025)], reference number [000001]. The authors declare no conflicts of interest.