Provenance Graphs for High-Pressure Experimental Evidence

Reliable deployment in diamond-anvil experiments with multi-instrument observations depends on more than obtaining a strong benchmark result. This conceptual analysis uses scientific provenance to study how claims travel from data to model output and then to action. Its central thesis is that the unit of assurance must be the linked history of sample preparation, pressure-temperature path, instrument output, code version, fit, and scientific claim. The reviewed evidence shows recurring risks from hidden distribution change, correlated evaluation error, missing provenance, and optimization objectives that omit downstream costs. In response, the article proposes a layered evaluation program combining controlled perturbations, subgroup and scenario analysis, repeated runs, calibration or selective prediction, and monitoring after release. It also asks who can inspect, override, and learn from failures. By integrating the assigned target papers with established scholarship, the synthesis clarifies which findings transfer across domains and which remain local to a benchmark, dataset, or experimental apparatus. The goal is a testable research program for bounded, traceable, and revisable systems.

1. Ma, Mingjun, et al. "MuSK: Multi-Scale Knowledge Learning for Provenance-Graph Anomaly Detection." *Computer Networks* 289 (2026): 112728.
2. Li, Yuanhao, et al. "BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models." *arXiv preprint arXiv:2605.09134* (2026).
3. Chen, Huawei, et al. "Possible H2O Storage in the Crystal Structure of CaSiO3 Perovskite." *Physics of the Earth and Planetary Interiors* 299 (2020): 106412.
4. Hu, Yuntong, et al. "LARGER: Lexically Anchored Repository Graph Exploration and Retrieval." *arXiv preprint arXiv:2605.16352* (2026).
5. Liao, Xiaojing, et al. "Acing the IOC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence." *Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security*, 2016, pp. 755-766.
6. Strom, Blake E., et al. *MITRE ATT&CK: Design and Philosophy*. MITRE Corporation, 2018.
7. Wilkinson, Mark D., et al. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." *Scientific Data*, vol. 3, 2016, article 160018.
8. King, Samuel T., and Peter M. Chen. "Backtracking Intrusions." *Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles*, 2003, pp. 223-236.
9. Milajerdi, Sadegh M., et al. "HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows." *2019 IEEE Symposium on Security and Privacy*, 2019, pp. 1137-1152.
10. Han, Xueyuan, et al. "UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats." *Network and Distributed System Security Symposium*, 2020.
11. Pasquier, Thomas, et al. "Runtime Analysis of Whole-System Provenance." *Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security*, 2017, pp. 1601-1614.
12. Schlichtkrull, Michael, et al. "Modeling Relational Data with Graph Convolutional Networks." *The Semantic Web*, Springer, 2018, pp. 593-607.
13. Hamilton, William L., Rex Ying, and Jure Leskovec. "Inductive Representation Learning on Large Graphs." *Advances in Neural Information Processing Systems*, vol. 30, 2017.
14. Velickovic, Petar, et al. "Graph Attention Networks." *International Conference on Learning Representations*, 2018.