ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
How MARIA OS's Meta-Insight turns unbounded recursive self-improvement into convergent self-correction while preserving governance constraints
Recursive self-improvement (RSI) — an AI system improving its own capabilities — is both promising and risky. Unbounded RSI raises intelligence-explosion concerns: a system improving faster than human operators can evaluate or constrain. This paper presents governed recursion, a Meta-Insight framework in MARIA OS for bounded RSI with explicit convergence guarantees. We show that the composition operator M_{t+1} = R_sys ∘ R_team ∘ R_self(M_t, E_t) implements recursive improvement in meta-cognitive quality, while a contraction condition (gamma < 1) yields convergence to a fixed point instead of divergence. We also provide a Lyapunov-style stability analysis where Human-in-the-Loop gates define safe boundaries in state space. The multiplicative SRI form, SRI = product_{l=1..3} (1 - BS_l) * (1 - CCE_l), adds damping: degradation in any one layer lowers overall autonomy readiness. Across simulation and governance scenarios, governed recursion retained 89% of the unconstrained improvement rate while preserving measured alignment stability.
Why controlling RAG accuracy through responsibility structure outperforms Top-k optimization alone
Many RAG systems optimize retrieval quality primarily through Top-k tuning and embedding similarity. This paper adds a governance-oriented approach: responsibility-tiered gates that adjust validation intensity by risk classification. The framework reports an 82% hallucination-rate reduction on enterprise document corpora while maintaining sub-second response times for low-risk queries.
Responsibility decomposition-point control for enterprise AI agents
When an AI agent modifies production code, calls external APIs, or alters contracts, responsibility boundaries must remain explicit. This paper formalizes fail-closed gates as a core architectural primitive for responsibility decomposition in multi-agent systems. We derive gate configurations via constrained optimization and report that a 30/70 human-agent ratio preserved 97.1% responsibility coverage while reducing decision latency by 58%.
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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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