Safety Invariance in Stochastic AI Moderation Pipelines: A Physics-Informed Empirical Study

Safety Invariance in Stochastic AI Moderation Pipelines: A Physics-Informed Empirical Study | Zenodo Skip to main Communities My dashboard Log in Sign up Zenodo is currently experiencing slowness and intermittent outages due to heavy automated traffic from bots and AI crawlers. We are aware of the problem, and our team is focused on stabilizing the service. Thank you for your patience. Published May 7, 2026 | Version v1 Preprint Open Safety Invariance in Stochastic AI Moderation Pipelines: A Physics-Informed Empirical Study Authors/Creators Serra-Taylor, Jose (Researcher) 1 Serra-Ramirez, Abbygail (Researcher) 2, 3 Irizarry-Borrero, Luis (Researcher) 2, 4 Perez-Torres, Solimar (Researcher) 1 Show affiliations 1.

NVARIS, Puerto Rico 2.

University of Central Florida, Orlando 3.

Master of Science in Mechanical Engineering (MSME) Program 4.

Master in Robotics and Autonomous Systems (MS) Program Description The deployment of Large Language Models (LLMs) in critical applications presents a fundamental tension between generative stochasticity and the requirement for deterministic safety guarantees. This work extends the physics-informed phenomenology of safety invariance—previously documented in physical control systems—to the domain of AI moderation pipelines. By framing semantic generation as a stochastic diffusion process, we analyze 2,735 samples within a prompt-filtering architecture to provide empirical evidence that safety emerges not merely as a statistical artifact, but as a dynamic forward invariant consistent with reflecting-boundary Fokker-Planck dynamics. This invariance is characterized by three robust phenomenological signatures: (1) strict distributional truncation with zero observed violations across all samples (Clopper-Pearson 95% upper bound: ), together with strong boundary-layer accumulation under crisis-level semantic pressure (242/560, 43.2%; ); (2) redirection dynamics consistent with Skorokhod-type reflection at the boundary; and (3) a scaling law in intervention intensity across 256 active correction events, revealing an effective repulsive response near the safety boundary. These findings support the hypothesis that forward-invariant safety manifolds are substrate-independent and provide a phenomenological basis for informing certification frameworks for stochastic AI systems through deterministic boundary enforcement. Files Serra_et_al_Safety_Invariance_in_Stochastic_AI_Moderation_Pipelines_A_Physics-Informed_Empirical_Study.pdf Files (936.7 kB) Name Size Download all Serra_et_al_Safety_Invariance_in_Stochastic_AI_Moderation_Pipelines_A_Physics-Informed_Empirical_Study.pdf md5:0250bc66324cca0eaa91a4825ffb9cd2 936.7 kB Preview Download Additional details Related works Continues Preprint: 10.5281/zenodo.20073543 (DOI) Dates Submitted 2026-05-07 References T. B. Brown et al., "Language Models are Few-Shot Learners," Adv. Neural Inf. Process. Syst., vol. 2020-December, May 2020, Accessed: Jan. 19, 2026. [Online]. Available: https://arxiv.org/pdf/2005.14165 L. Ouyang et al., "Training language models to follow instructions with human feedback," Adv. Neural Inf. Process. Syst., vol. 35, Mar. 2022, Accessed: Jan. 19, 2026. [Online]. Available: https://arxiv.org/pdf/2203.02155 Y. Bai et al., "Constitutional AI: Harmlessness from AI Feedback," Dec. 2022, Accessed: Jan. 19, 2026. [Online]. Available: https://arxiv.org/pdf/2212.08073 A. Wei, N. Haghtalab, and J. Steinhardt, "Jailbroken: How Does LLM Safety Training Fail?," Adv. Neural Inf. Process. Syst., vol. 36, Jul. 2023, Accessed: Jan. 19, 2026. [Online]. Available: https://arxiv.org/pdf/2307.02483 J. García and F. Fernández, "A Comprehensive Survey on Safe Reinforcement Learning," J. Mach. Learn. Res., vol. 16, pp. 1437–1480, 2015. J. Achiam, D. Held, A. Tamar, and P. Abbeel, "Constrained Policy Optimization," in 34th International Conference on Machine Learning, Sydney, Australia: PMLR 70, 2017. J. Serra-Taylor, A. Serra-Ramirez, and L. Irizarry-Borrero, "Physics-informed phenomenology of safety invariance in learning-based control across thermal, electrical, physiological, and financial domains (Version 2)". Zenodo, 2026. https://doi.org/10.5281/zenodo.20073543 A. D. Ames, P. Tabuada, G. Notomista, Koushil Sreenath, Samuel Coogan, and M. Egerstedt, "Control Barrier Functions: Theory and Applications," in Control Barrier Functions: Theory and Applications, EUCA, Ed., Napoli, Italia: 18th European Control Conference (ECC), Jun. 2019, pp. 3420–3431. A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, "Control Barrier Function Based Quadratic Programs for Safety Critical Systems," IEEE Trans. Automat. Contr., vol. 62, no. 8, pp. 3861–3876, Dec. 2017, doi: 10.1109/TAC.2016.2638961 Y. Chow, O. Nachum, E. Duenez-Guzman, and M. Ghavamzadeh, "A lyapunov-based approach to safe reinforcement learning," Adv. Neural Inf. Process. Syst., vol. 2018-December, pp. 8092–8101, May 2018, [Online]. Available: http://arxiv.org/abs/1805.07708 H. Risken, The Fokker-Planck Equation: Methods of Solution and Applications. Springer, 1996. OpenAI, "Moderation Guide," 2026. [Online]. Available: https://platform.openai.com/docs/guides/moderation J. Kaplan et al., "Scaling Laws for Neural Language Models," Jan. 2020, Accessed: Mar. 13, 2026. [Online]. Available: http://arxiv.org/abs/2001.08361 P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever, "Deep double descent: where bigger models and more data hurt*," J. Stat. Mech. Theory Exp., vol. 2021, no. 12, p. 124003, Dec. 2021, doi: 10.1088/1742-5468/ac3a74 A. Power, Y. Burda, H. Edwards, I. Babuschkin, and V. Misra, "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets," Jan. 2022, Accessed: Mar. 13, 2026. [Online]. Available: http://arxiv.org/abs/2201.02177 C. J. Clopper and E. S. Pearson, "the Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial," Biometrika, vol. 26, no. 4, pp. 404–413, 1934, doi: 10.1093/biomet/26.4.404 Y. Wolf, N. Wies, O. Avnery, Y. Levine, and A. Shashua, "Fundamental Limitations of Alignment in Large Language Models," Jun. 2024, Accessed: Mar. 13, 2026. [Online]. Available: https://arxiv.org/pdf/2304.11082v6 D. Davidad et al., "Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems," Jul. 2024, Accessed: Mar. 13, 2026. [Online]. Available: https://arxiv.org/pdf/2405.06624v2 168 Views 145 Downloads Show more details All versions This version Views Total views 168 168 Downloads Total downloads 145 145 Data volume Total data volume 168.6 MB 168.6 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Safe reinforcement learning physics-informed machine learning forward invariance safety-critical control reflected stochastic dynamics constraint enforcement Control Barrier Functions empirical validation Details DOI DOI Badge DOI 10.5281/zenodo.20075551 Markdown [](https://doi.org/10.5281/zenodo.20075551) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.20075551.svg :target: https://doi.org/10.5281/zenodo.20075551 HTML <a href="https://doi.org/10.5281/zenodo.20075551"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.20075551.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.20075551.svg Target URL https://doi.org/10.5281/zenodo.20075551 Resource type Preprint Publisher Zenodo Languages English Rights License Creative Commons Attribution Non Commercial Share Alike 4.0 International (CC-BY-NC-SA 4.0) No further description. Copyright 2026 Jose Serra-Taylor, Abbygail Serra-Ramirez, Luis Irizarry-Borrero, Solimar Perez-Torres. All rights reserved. Patent Pending. Citation Export Technical metadata Created May 7, 2026 Modified May 8, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies

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