TAG ARCHIVE
SEO-research
10 MARIA OS blog articles tagged SEO-research, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Meta-Insight Under Distribution Shift: Change-Point Governance Loops for Enterprise Agentic Systems
An operational architecture for detecting non-stationarity, throttling unsafe adaptation, and restoring decision quality under drift
This article outlines change-point detection, bounded policy updates, and fail-closed escalation for distribution-shift governance.
Detecting Groupthink in Agent Teams: Persistent Homology for Blind-Spot Alerts
Topological signals expose hidden coverage gaps and groupthink risk that pairwise diversity metrics can miss
Persistent homology tracks coverage holes across scales to flag latent team blind spots earlier.
Counterfactual Escalation Policy: Meta-Insight Routing for High-Impact Human Review
Estimate intervention value before handoff to reduce unsafe approvals and unnecessary escalations
Escalation is triggered when estimated causal benefit exceeds review cost, not by confidence alone.
Confidence-Evidence Coupling for Agentic Governance: A Calibration Law for Safer Decisions
Couple confidence outputs to evidence sufficiency and contradiction pressure to reduce silent high-certainty failures
The coupling law ties confidence to evidence quality and provenance, improving escalation precision under uncertainty.
Productive Disagreement Protocol for Agent Teams: Structured Dissent for Higher-Quality Decisions
Operationalize evidence-backed dissent, validation diversity, and anti-groupthink interventions
Structured disagreement channels dissent into testable claims, improving decision quality without collapsing throughput.
Memory Stratification for AI Governance: A Rate-Distortion Framework for Retention Decisions
Use information theory to decide what enterprise AI systems should remember, summarize, or discard
Rate-distortion memory policy retains high-utility context while limiting latency, privacy risk, and contradiction noise.
Securing Recursive AI Feedback Loops: Adversarial Reflexivity Hardening for Meta-Insight Systems
Defense framework for prompt injection, feedback poisoning, and policy-hijack attacks in self-improving loops
Layered provenance checks, anomaly scoring, and quarantine rules harden adaptive loops while preserving auditability.
Causal Analysis of Organizational Learning Rate: OLR Decomposition for Intervention Attribution
From correlation-heavy dashboards to intervention-level attribution in meta-insight governance systems
Causal OLR decomposition attributes observed learning-rate gains to specific interventions, improving budget and policy allocation decisions.
Meta-Insight ROI Model: Value-at-Reflection Economics for Agentic Companies
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.
Causal-Temporal Knowledge Graph for AI Governance: Path-Specific Responsibility Attribution
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.