Repairable Code and Irreducible Genetic Uncertainty
摘要
The boundary between useful assistance and an unsupported claim becomes visible when passing software tests mistaken for biological confirmation. This literature-based conceptual analysis does not report a new experiment. The focal evidence concerns how exploratory cross-trait analysis might nominate candidate regions shared by hearing loss and age-related disease and connect them to inner-ear contexts. Execution verifies a property of code against an environment and a set of tests. It does not automatically verify the scientific question, the endpoint definition, or untested requirements. Connect tests to substantive requirements and include invalid-data fixtures. Audit repaired code for changed meaning, not just successful completion. The proposed comparison concerns analysis scripts, phenotype coding, candidate regions; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between candidate regions and cellular annotations and any stronger downstream decision.
参考
Yu, Xiao, et al. “Exploratory Cross-Trait Genetic Analysis of Hearing Loss and Age-Related Diseases Nominates Cardiometabolic Candidate Regions and Inner-Ear Cellular Contexts.” Intelligent Translational Medicine, 2026, https://doi.org/10.1016/j.itm.2026.100002.
Li, Yuanhao, et al. “BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models.” arXiv, version 4, 18 Sept. 2026, https://arxiv.org/abs/2605.09134v4. Preprint.
Dai, Junjian, et al. “Interaction between Noise Exposure and Phenotypic Age Acceleration in Hearing Loss: An NHANES Study.” ENT Updates, vol. 16, no. 3, 2026, pp. 1–12, https://doi.org/10.54963/entu.v16i3.100576.
Sang, Yinghao. “Robustness of Fine-Tuned LLMs under Noisy Retrieval Inputs.” 2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), IEEE, 2025, pp. 417–20, https://doi.org/10.1109/aiea66061.2025.11160531.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.
Rusu, Andrei A., et al. “Policy Distillation.” arXiv, 2015, arXiv:1511.06295.
Robertson, Stephen, and Hugo Zaragoza. “The Probabilistic Relevance Framework: BM25 and Beyond.” Foundations and Trends in Information Retrieval, vol. 3, no. 4, 2009, pp. 333–89.
Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–74.
Karpukhin, Vladimir, et al. “Dense Passage Retrieval for Open-Domain Question Answering.” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020, pp. 6769–81.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.
Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data, vol. 3, 2016, article 160018.
Hernán, Miguel A., and James M. Robins. Causal Inference: What If. Chapman & Hall/CRC, 2020.
Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.