ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Formalizing gate strength as a continuous control variable to minimize the combined cost of false positives, missed detections, and investigation delay in AML compliance pipelines
AML programs face a costly tradeoff between false positives, missed detections, and investigation delay. This paper formalizes AML detection as constrained loss minimization over gate strength `g`, derives gate configurations with Lagrangian methods under BSA/FATF/EU AMLD constraints, and reports that MARIA OS responsibility gates reduced total compliance cost by 47% while maintaining required detection thresholds in evaluated settings. The framework treats gate strength as a continuous control variable rather than a binary switch.
Effective throughput is shipped output adjusted by rework return
Enterprise AI systems often optimize completion rate while under-accounting for rework. A system with high completion but high rework can have much lower net throughput. This paper models effective throughput as F_effective = F_short * (1 - Rework) and models rework decay with gate quality as R(g) = R_0 * e^(-beta*g). We derive an optimal gate strength g* that maximizes net throughput under the throughput-quality tradeoff.
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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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