Passage Influence in Genetic Evidence Summaries
Abstract
Passage Influence in Genetic Evidence Summaries addresses a narrow but consequential interpretive problem: one highly ranked passage controlling a broad claim. 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. Attribution asks what evidence influenced a statement and whether that evidence supports it. Those are different tests. Remove and replace passages while monitoring individual claims. Pair influence analysis with qualified support judgments, retaining failures in either dimension. The proposed comparison concerns candidate regions, source snippets, generated interpretation; 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.
References
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.
Wu, CongCong, et al. “An Oxidative Stress Biomarker for Noise-Associated Stroke: Evidence from Human and Mice.” Noise and Health, vol. 28, no. 133, 2026, pp. 945–58, https://doi.org/10.4103/nah.nah_239_25.
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.
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.
Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.
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.