TAG ARCHIVE
MARIA-VOICE
2 MARIA OS blog articles tagged MARIA-VOICE, 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.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Voice-Driven Agentic Avatars: A Recursive Self-Improvement Framework for Autonomous Intellectual Task Delegation
Formal convergence analysis, delegation-completeness theorems, and safety bounds for voice-mediated multi-agent governance systems
We present the Voice-Driven Agentic Avatar (VDAA) framework, a formal model of voice-mediated intellectual task delegation in multi-agent systems. The framework unifies full-duplex voice interaction, recursive self-improvement cycles, and hierarchical agent coordination under a single convergence analysis. We show that delegation loops converge to fixed-point task allocations under bounded cognitive-fidelity loss, establish delegation completeness for finite task algebras, and derive safety bounds through a three-gate Lyapunov formulation. Evaluation on MARIA VOICE reports 94.7% delegation accuracy, sub-200ms voice-to-action latency, and zero safety-gate violations across 12,000 delegated tasks.
Voice-Driven Agentic Avatars: Foundational Theory for High-Cognition Task Delegation with Recursive Improvement
From formal VDAA definitions to triple-gate voice governance in the MARIA VOICE architecture
High-cognition tasks such as strategy, audit review, proposal design, and structured brainstorming are difficult to scale through human effort alone. This paper presents a formal framework for Voice-Driven Agentic Avatars (VDAA): voice-mediated interaction, recursive self-improvement loops (OBSERVE -> ANALYZE -> REWRITE -> VALIDATE -> DEPLOY), four-team action routing, and rolling-summary support for long sessions. We define convergence conditions for cognitive fidelity Phi(A,H), formal safety boundaries for triple-gate voice governance, and a responsibility-conservation extension for voice-driven operations. The quantitative figures in this article should be read as replay and simulation outputs over 12 operating contexts, while the current MARIA VOICE implementation provides the underlying streaming voice pipeline, tool routing, and summary mechanisms.