ASI-Arch Safety Framework

• ASI-Arch Safety Framework is a unified safety architecture that blends classical reliability engineering with AI-specific risk analyses to guarantee system-level safety.
• It decomposes system functionality into multi-layered views, enabling dynamic risk management through adaptive safety supervisors and redundancy mechanisms.
• Hybrid safety methodologies extend traditional FMEA/FTA to AI by incorporating white-box transparency and agentic risk taxonomies for robust hazard mitigation.

The ASI-Arch Safety Framework encompasses a diverse collection of architectural methodologies, taxonomies, and dynamic protocols for assuring safety in autonomous systems—including artificial superintelligence (ASI), agentic AI, autonomous vehicles, robotics, aviation systems, and LLM infrastructures. These frameworks unify safety as an emergent architectural property, integrate classical reliability engineering with AI-specific failure modes, and operationalize rigorous, adaptive safety and security controls through both static and runtime mechanisms.

1. Foundational Principles: Safety as an Architectural Property

Safety within the ASI-Arch framework is defined as an emergent system property arising from architectural choices, where system-level risk is constrained below an established threshold ($P_\mathrm{sys}^\mathrm{req}$), and assurance processes meet or exceed required levels (e.g., DAL$_\mathrm{sys}$ in aviation). Architectures are formally modeled as networks of components $(C)$ and interfaces $(I)$, with quantifiable per-component failure rates $(\lambda_i)$ and diagnostic coverage $(DC_i)$. Formal reliability block diagrams (RBDs) and failure probability formulas are used to analyze and structure safety goals and architectural patterns, ensuring that even in the presence of AI/ML-induced causal opacity and behavioral dynamism, safety assurance remains tractable and measurable (Fenn et al., 2023).

2. Multi-Level Architectural Decomposition and Adaptation

The ASI-Arch approach structures systems across multi-layered abstractions and viewpoints:

• Functional (Logical) Viewpoint: Classical decomposition into information flows and blocks (e.g., sense, plan, act), suited for traceability but not performance guarantees.
• Capability (Skill) Viewpoint: Directed acyclic graphs representing runtime dependencies among capabilities (e.g., perception, prediction, decision, actuation), each annotated with performance measures and real-time scoring functions for behavioral risk.
• Behavioral Safety Viewpoint: Cross-cutting formalization of hazards, safety goals, risk-minimal states, and scenario assumptions, bridging functional and capability demands (Bagschik et al., 2018).

Dynamic safety supervisors (e.g., dual MAPE-K loops in autonomous driving) architect systems with nominal and independent safety channels. Supervisors monitor internal health (ISCs) or external risk envelopes (ESCs), trigger takeovers in hazardous states (e.g., minimal risk condition maneuvers), and avoid single-point failures by configurable redundancy (Törngren et al., 2019).

3. Hybrid Safety Methodologies for AI-Centric Systems

The HySAFE-AI framework extends traditional FMEA and FTA paradigms to handle continuous latent spaces, opaque causality, and context-dependent errors inherent in foundational models (LLMs/VLMs). Key adaptations include:

• White-box transparency: Insisting on explicit access to major AI blocks and their latent interfaces, even with closed-source models.
• Multi-level abstraction of FMEA/FTA components: Treating raw inputs, latents, transformers, planners, and decoders as distinct analyzable units.
• AI-specific failure taxonomy: Mapping standard FMEA guidewords to generalized AI failure modes (“hallucination,” “misprediction,” “quantization artifact”) and domain-specific hazards.
• Risk Priority Number (RPN):

$RPN_i = S_i \times O_i \times D_i$

where $S$ (Severity), $O$ (Occurrence), $D$ (Detection) are rated on a 1–10 scale.

Augmented FMEA/FTA workflows are demonstrated on unified GAIA-2 / GenAD stacks for autonomous driving, quantifying fault modes (e.g., latent denoiser hallucination, dataset staleness), proposing mitigations (uncertainty-calibrated monitors, quantization-aware training, active learning pipelines), and demonstrating significant RPN reductions post-mitigation (Pitale et al., 23 Jul 2025).

4. Dynamic Risk Management and Self-Improving Runtime Safety

ASI-Arch frameworks incorporate adaptive controls for agentic and LLM-driven systems, using dynamic feedback loops, risk agents, and autonomous policy synthesis:

• System Model: $S = (M, O, T, D, A, H)$, where $M$ are models, $O$ orchestrators, $T$ tools, $D$ data pipelines, $A$ auxiliary risk agents, and $H$ human oversight.
• Objective Function:

$$R^* = \underset{R}{\arg\min}\; \mathbb{E}[R(S;R)],\quad U(S;R)\ge U_0,\quad L(S;R)\le L_0$$

where risk ($R$), utility ($U$), and latency ($L$) are the main constraints.

• Self-Improving Safety Framework (SISF): Embeds a base LLM (Warden), adjudicator (GPT-4o), policy synthesis module (GPT-4 Turbo), and an adaptive policy store in a continuous learning architecture. Breach detection results in policy synthesis and deployment, driving down attack success rates ($ASR$) and false positive rates ($FPR$) via formal metrics:

$$\text{ASR} = \frac{N_{\mathrm{breaches}}}{N_{\mathrm{adv}}},\quad \text{FPR} = \frac{N_{\mathrm{FP}}}{N_{\mathrm{benign}}}$$

Empirical results confirm progressive risk reduction (ASR from 100% to 45.58%) with zero FPR on benign cases, via synthesis of over 234 policies (Slater, 10 Nov 2025).

• Agentic Risk Taxonomy: Enumerates Tool Misuse (TM), Cascading Actions (CA), Control Amplification, Memory Poisoning, Emergent Goal Drift, and Privilege Escalation as formal risk categories, each with quantitative scoring and mitigation prioritization (Ghosh et al., 27 Nov 2025).

5. Property-Driven Pattern Catalogues and Design Guidelines

A core facet of ASI-Arch frameworks is the creation and maintenance of living architectural pattern catalogues, driven by safe-by-design properties:

• Patterns are captured with block diagrams, safety formulas ($P_\mathrm{fail}$), assurance allocations (DALs), diagnostic coverage, domain constraints, and explicit mapping of AI/ML-related limitations.
• Algorithmic pattern selection is executed by maximizing attribute-weighted scores subject to global safety constraints:

  for each function f_j: select p_j in CandidatePatterns_j such that P_fail(p_j) ≤ P_fj and DAL_max ≤ DAL_j

• Recommended guidelines emphasize high-assurance wrappers and monitors, formalization of AI/ML-specific failure modes, partitioning for domain completeness, and periodic ingestion of new research and operational data into the catalogue (Fenn et al., 2023).

6. Governance, Law Enforcement, and Security-Coexistence Architectures

Advanced ASI-Arch paradigms integrate explicit governance and incentive mechanisms to manage superintelligent or agentic systems:

• Kill-ASI devices provide irreversible mortality guarantees for ASI instances ($\forall\,e\,\exists\,s: \text{Terminate}(e,s)$).
• Human Protection Layers, Security Separation Units (hardware/software watchdogs, key-safes, content/network/process isolation), and ASI Shelters enforce technical separation, law compliance, and quota-restricted operation.
• Rule-of-Law Monitors aggregate violation evidence and interface with governance systems—including economic token-based incentives, punishment schedules, pardons, and arbitration protocols—to manage ASI behavior.
• All inter-component messages and quota adjustments utilize hardware-rooted, unbreakable encryption (Key-Safes, EDUs).
• The architecture is structured such that catastrophic breaches result in immediate global Kill-ASI broadcasts, enforcing mortal vulnerability and lawful coexistence (Wittkotter et al., 2021).

7. Future Directions and Standardization Implications

Ongoing development in the ASI-Arch Safety Framework advocates:

• Embedding architectural transparency, multi-level FMEA/FTA covering latent and explicit modules, and uncertainty-informed risk metrics into evolving standards (ISO/PAS 8800, ISO 21448/SOTIF, ISO/IEC TS 22440-1/25223).
• Leveraging AI-driven red teaming, sandboxed environments, and open-benchmark datasets for continuous risk profiling and mitigation effectiveness validation.
• Advancing agentic safety research by deploying and sharing real-world trace datasets (e.g., NVIDIA Nemotron-AIQ) to support benchmarking, mitigation training, and community-driven extension (Ghosh et al., 27 Nov 2025).

The ASI-Arch Safety Framework thus synthesizes rigorous, quantifiable approaches from classical reliability engineering and AI-specific failure analysis, enabling robust, auditable, and continually improving safety assurance for next-generation autonomous and agentic systems.