TAG ARCHIVE
infinite-regress
2 MARIA OS blog articles tagged infinite-regress, 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.
Terminating Infinite Meta-Cognitive Regress: A Scope-Bounded Proof for Multi-Agent Self-Monitoring
A formal proof that MARIA OS hierarchical meta-cognition avoids infinite self-reference through scope stratification, establishing well-founded descent on reflection depth with links to fixed-point theory and Gödel's incompleteness theorems
The infinite regress problem - who watches the watchers? - is a classic objection to self-monitoring systems. In multi-agent architectures, the challenge intensifies: each agent must assess whether peer self-assessments are reliable, creating a potentially unbounded tower of mutual meta-evaluation. This paper provides a formal termination proof for MARIA OS hierarchical meta-cognition, showing that the three-level reflection composition R_sys ∘ R_team ∘ R_self terminates in bounded computational steps through scope stratification in the MARIA coordinate hierarchy. We connect the result to the Tarski-Knaster and Banach fixed-point theorems, and show that this scope-bounded design avoids Gödelian self-reference traps that block unrestricted self-consistency proofs.
Structural Architecture of Meta-Insight: Three-Layer Meta-Cognitive Decomposition Aligned with Organizational Hierarchy
Why meta-cognition in multi-agent systems should be decomposed by organizational scope, and how MARIA coordinates provide natural reflection boundaries
Meta-cognition in autonomous AI systems is often modeled as a monolithic self-monitoring layer. This paper argues that monolithic designs are structurally weak for multi-agent governance and introduces a three-layer architecture (Individual, Collective, System) that decomposes reflection by organizational scope. We map these layers to MARIA coordinates: Agent, Zone, and Galaxy. The update operator M_{t+1} = R_sys ∘ R_team ∘ R_self(M_t, E_t) forms a contraction under Banach fixed-point conditions when layer operators are Lipschitz-bounded, yielding convergence to a stable meta-cognitive equilibrium. We also show how scope constraints bound self-reference depth and mitigate infinite-regress failure modes. Across 12 MARIA OS deployments (847 agents), this architecture reduced collective blind spots by 34.2% and improved organizational learning rate by 2.1x versus flat baselines.