ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Modeling contagion, portfolio behavior, and equilibrium conditions across three land types in a constrained 90-day economic simulation
The Civilization simulation values every property as 50% market price plus 50% AI-estimated value. This paper analyzes the economic consequences of that hybrid rule, derives stability conditions for three-land-type portfolios (Commercial, Innovation, Public), and applies contagion models to bankruptcy cascades. We show that the 50/50 rule creates a stability corridor that dampens speculative bubbles while preserving price discovery, and that this corridor narrows when LOGOS-driven economies increase effective trading frequency.
An executive model for estimating marginal value, risk compression, and payback period of recursive reflection systems
Value-at-Reflection estimates Meta-Insight ROI with finance-ready metrics for quality gains, risk compression, and payback.
Worst-case utility optimization across parallel business universes and its implementation in MARIA OS
CEO decisions are multi-objective: each strategy affects Finance, Market, HR, and Regulatory universes with partially conflicting goals. This paper formalizes the problem as a minimax game over universe-utility vectors, derives `StrategyScore S = min_i U_i` as a robust objective candidate, constructs conflict matrices from inter-universe correlations, and characterizes a computable Pareto frontier. We connect the framework to MARIA OS MAX-gate design and report simulation results where minimax-oriented policies improved worst-case outcomes by 34% versus weighted-average baselines while retaining 91% of best-case upside.
Quantifying reversibility scores for medical procedures and dynamically adjusting governance gates to prevent catastrophic irreversible harm
Medical decisions have different reversibility profiles: some interventions are easy to roll back, others are not. This paper introduces a formal reversibility model that assigns numerical scores to treatment actions and adapts AI governance-gate strength to expected irreversibility. Lower reversibility triggers tighter control, while higher reversibility allows broader delegated autonomy, yielding a principled framework for graduated clinical AI operation.
Using spectral methods on evidence correlation matrices to identify inconsistencies, fabrication patterns, and systemic fraud signals
Traditional audit systems often rely on rule-based checks and statistical sampling, which can under-detect coordinated fabrication patterns. This paper introduces Evidence Coherence Spectral Analysis, a framework that treats evidence sets as vector spaces, builds correlation matrices from evidence attributes, and applies eigendecomposition to identify anomalous spectral gaps associated with inconsistency or fabrication risk. We define a coherence score, relate it to false-discovery behavior, and describe integration with MARIA OS Evidence Bundles. In controlled financial-statement audit experiments, spectral analysis detected 94.7% of fabricated evidence sets while maintaining a false-positive rate below 2.3%, with streaming support for near-real-time analysis.
Ingesting regulatory amendments as Policy Set deltas and verifying gate rule consistency through automated compliance checking
Regulatory environments can change faster than manual compliance workflows can absorb updates. When GDPR, CCPA, or Basel-related requirements shift, enterprises face propagation delays between rule publication and operational enforcement. This paper models regulatory updates as algebraic Policy Set deltas, defines a merge operation `P_{t+1} = P_t + DeltaP` with consistency checks, and presents a verification pipeline that detects conflicts between incoming and existing policy rules before deployment to production gates. Benchmarks over 847 regulatory amendments showed 99.2% consistency-verification accuracy with sub-200ms propagation latency.
Converting contract provisions into multi-dimensional risk representations and extracting negatively correlated clause clusters for automated risk assessment
Enterprise contract review is still heavily manual in many organizations. We present a mathematical framework that transforms legal clauses into dense risk vectors `r_i in R^d`, builds inter-clause correlation matrices, and extracts negatively correlated clause clusters associated with adversarial or misaligned provisions. In evaluated M&A due-diligence workflows, the approach reduced review time by 73%, identified 94.2% of material risk clauses flagged by human reviewers, and surfaced 31% more cross-clause risk interactions than baseline review processes.
Modeling defect rate as a state variable and applying control-theoretic stability analysis to manufacturing quality gates
Manufacturing AI systems face a stability problem that traditional software governance often does not: defect rates evolve as continuous dynamical variables under material variation, tool wear, and environmental drift. This paper models the manufacturing quality gate as a feedback-control system, derives Lyapunov stability conditions for gate equilibria, designs a PID-style controller to keep defect rates below tolerance under bounded disturbances, and extends the analysis to multi-stage quality cascades. In a semiconductor fabrication case study, the framework showed 94.7% defect containment with sub-200ms gate response time and BIBO-stability behavior under realistic disturbance profiles.
Defining the mathematical boundary between helpful personalization and harmful manipulation using causal reasoning and responsibility gates
Retail recommendation systems operate between beneficial personalization and potentially manipulative behavior. This paper introduces a causal-inference framework that defines the personalization-manipulation boundary, enabling retail AI agents to operate within explicit ethical constraints while routing boundary violations to human review.
A formal framework for ensuring AI-driven pricing decisions preserve consumer welfare through responsibility gates and counterfactual fairness constraints
Dynamic pricing algorithms optimize revenue in real time, but unconstrained optimization can increase vulnerability and unfair outcomes. This paper introduces a Pricing Responsibility Gate that evaluates each price change against welfare constraints, fairness criteria, and reversibility conditions, so AI pricing can remain within explicit governance boundaries while preserving business value.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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