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.
Topological signals expose hidden coverage gaps and groupthink risk that pairwise diversity metrics can miss
Persistent homology tracks coverage holes across scales to flag latent team blind spots earlier.
Operationalize evidence-backed dissent, validation diversity, and anti-groupthink interventions
Structured disagreement channels dissent into testable claims, improving decision quality without collapsing throughput.
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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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