TAG ARCHIVE
capability-gap
4 MARIA OS blog articles tagged capability-gap, 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。
Self-Extending Agent Architecture: Capability Gap Detection, Tool Synthesis, and Autonomous Evolution Under Governance Constraints
Agents that recognize their own limitations and autonomously build the tools they need — within the safety boundaries of an operating system
Traditional AI agents are bounded by the tools humans provide. When an agent encounters a task outside its toolset, it halts and waits. This paper introduces the Self-Extending Agent Architecture (SEAA), where agents detect their own capability gaps, synthesize new tools through code generation, validate those tools in sandboxed environments, and register them into the OS runtime — all under human-governed safety constraints. We formalize the agent state model X_t = (C, T, M, R), derive the self-extension equation X_{t+1} = E_t ∘ G_t ∘ J_t(X_t), prove Capability Monotonicity under validation gates, and demonstrate the architecture within MARIA OS's hierarchical coordinate system.
自己拡張型Agentアーキテクチャ — 能力不足を自ら認識し、ツールを自律生成するOS設計
Agentが自身の限界を検知し、コード生成でツールを合成し、サンドボックスで検証し、OSランタイムに登録する — すべてガバナンス制約の下で
従来のAIエージェントは、人間が提供したツールセットに束縛される。未対応タスクに遭遇すると停止し、人間の介入を待つ。本論文では、Self-Extending Agent Architecture（SEAA）を提案する。エージェントが自律的に能力ギャップを検出し、構造化コード生成でツールを合成し、サンドボックス環境で検証し、OSランタイムに登録するフレームワークである。エージェント状態モデル X_t = (C, T, M, R) を形式化し、自己拡張方程式 X_{t+1} = E_t ∘ G_t ∘ J_t(X_t) を導出し、検証ゲート下での能力単調性定理を証明する。MARIA OSの階層座標系における具体的な実装を示す。