TAG ARCHIVE
self-awareness
3 MARIA OS blog articles tagged self-awareness, 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.
Capability Gap Detection: The Metacognitive Layer That Enables Self-Extending Agents
How agents recognize what they cannot do and trigger autonomous self-extension through formal gap analysis
Self-extending agents require a prerequisite that most architectures ignore: the ability to know what they do not know. This paper formalizes capability gap detection as a metacognitive layer that compares required capabilities against the agent's capability model, classifies detected gaps, prioritizes them by urgency and impact, and decides whether to synthesize, request, delegate, or escalate. We introduce the capability coverage metric, gap entropy measure, and multi-agent gap negotiation protocol. Experimental results show that agents with formal gap detection achieve 4.1x fewer silent failures and 2.8x faster self-extension compared to agents relying on runtime error detection.
Capability Gap Detection — Agentが自分の能力不足を認識するメタ認知アーキテクチャ
形式的ギャップ分析を通じて、自分にできないことを認識し自律的な自己拡張をトリガーする方法
自己拡張型Agentには、ほとんどのアーキテクチャが無視する前提条件がある。自分に何ができないかを知る能力である。本論文はCapability Gap Detectionをメタ認知レイヤーとして形式化する。必要な能力をAgentの能力モデルと比較し、検出されたギャップを分類し、緊急度とインパクトで優先順位付けし、合成・要求・委任・エスカレーションの判断を下す。能力カバレッジメトリック、ギャップエントロピー測度、マルチAgent間ギャップ交渉プロトコルを導入する。
Metacognition in Agentic Companies: Why AI Systems Must Know What They Don't Know
Latent governance density, observable metacognitive coverage, and the stability bounds of self-governing enterprises
We formalize an agentic company as a graph-augmented constrained Markov decision process G_t = (A_t, E_t, S_t, Pi_t, R_t, D_t), distinguish latent governance density D_t from observable constrained-candidate coverage D_hat_t on router-generated Top-K actions, and define damping via kappa_t = kappa(D_hat_t). The exact local contraction condition is (1 - kappa_t) lambda_max(W_t) < 1, while the buffered operating envelope lambda_max(W_t) < 1 - kappa_t preserves adaptation headroom. Governance constraints thereby function as organizational metacognition: each constraint is a point where the system observes its own behavior. Planet-100 simulations validate that buffered role specialization emerges in the intermediate governance regime.