ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
Transforming immutable decision records into queryable knowledge structures with principled temporal decay and cross-agent entity resolution
Enterprise governance platforms generate large audit trails that encode organizational decision-making, but those records are often difficult to query across multi-hop relationships. This paper presents a formal framework for constructing knowledge graphs from decision logs, including entity-resolution methods for noisy multi-agent audit data, temporal-decay functions for relevance-aware edge weighting, and compliance-oriented subgraph extraction. Experiments on MARIA OS audit corpora report 91.3% entity-resolution F1 across overlapping agent zones and 2.7x faster compliance-query response than relational baselines.
Mapping agents, decisions, and outcomes into continuous vector spaces to quantify competence through translational-distance geometry
Assessing AI-agent competence in enterprise governance requires moving beyond binary success/failure metrics toward a continuous, context-sensitive model. This paper introduces a knowledge-graph-embedding framework based on translational-distance models (TransE, RotatE) adapted to the MARIA OS responsibility space. Agents, decisions, and outcomes are embedded in a shared vector space, where competence is measured by distance between context-translated agent embeddings and ideal outcome embeddings. We formalize the geometry, derive governance-aware loss functions, analyze convergence behavior, and show that KGE-derived competence scores correlate with held-out success probability at r = 0.89.
Tensor-factorization methods for link prediction in incomplete governance graphs, with theoretical accuracy bounds across observability regimes
Enterprise knowledge graphs are inherently incomplete: undocumented responsibility links, informal decision chains, and cross-zone dependencies leave traceability gaps. This paper formulates governance-graph completion as a tensor-factorization problem under partial observability. We model the graph as a binary three-way tensor X in {0,1}^{n x n x r} (entities x entities x relations), apply CP decomposition to predict missing links, and derive theoretical accuracy bounds as a function of observability rate rho. On MARIA OS governance graphs, CP decomposition recovers 84.2% of withheld responsibility edges at 70% observability and surfaces 31 previously undocumented responsibility gaps in production.
A deep research framework for path-specific accountability, time-aware causality, and audit-grade explanation in enterprise AI
A temporal responsibility graph enables path-level causal attribution and faster, more reproducible root-cause analysis.
Spectral analysis of adjacency matrices reveals the optimal diffusion depth that maximizes signal-to-noise ratio in knowledge graph retrieval
Graph-based Retrieval Augmented Generation traverses knowledge graphs to gather context for language-model prompts. Each additional hop `h` in `A^h` can add useful context but also amplify noise through irrelevant paths. This paper models diffusion as matrix exponentiation with decay, derives signal-to-noise behavior by hop count using spectral decomposition, and identifies an optimal hop count `h*`. Across four enterprise knowledge graphs, the derived `h*` reduced hallucination rate by 43% versus fixed-depth traversal.
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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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