ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
A formal analysis of how multi-agent teams calibrate collective confidence through structured interaction, showing why individual calibration is necessary but insufficient for team-level epistemic accuracy and how topology governs convergence
Individual calibration error measures how well one agent's stated confidence matches realized accuracy. In collaborative settings, however, a distinct phenomenon appears: collective calibration, where team-level confidence must track team-level accuracy. This paper defines collective calibration error as a metric that cannot be reduced to aggregated individual calibration, proves that individually well-calibrated agents can still form a poorly calibrated team under certain interaction topologies, and derives sufficient graph conditions for convergence. We validate the framework on MARIA OS deployments with 623 agents across 9 zones, showing a 41.7% reduction in collective calibration error via topology-aware reflection scheduling.
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.
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.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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