TAG ARCHIVE
parallel-execution
3 MARIA OS blog articles tagged parallel-execution, 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.
Quality Assurance in Multi-Agent Parallel Execution: A Game-Theoretic Framework for Zone Partitioning and Gate Design
How responsibility gates and zone architecture can shift multi-agent conflicts from defection-prone dynamics toward cooperative equilibria
Multi-agent systems executing tasks in parallel face a quality challenge: conflict rates can grow quadratically with agent count. This paper presents a game-theoretic framework showing how responsibility gates and zone partitioning reduce conflict pressure while retaining high task completion. In evaluated settings, the design reported over 91% conflict-rate reduction with 98.7% task completion.
Multi-Agent Quality Convergence: A Probabilistic Model of Boundary Violations and Merge Failures in Parallel Execution
Quality can scale when boundaries are explicit: a formal model showing architecture, not raw agent count, is the main bottleneck
Multi-agent parallelism can improve throughput but introduces two quality risks uncommon in sequential pipelines: boundary violations (overlapping scopes) and merge failures (integration errors). We derive a total-success model `P(total) = Π(p_i) · (1 - q_merge) · (1 - q_overlap)` and analyze conditions under which quality remains stable as scale increases. The framework highlights that quality depends primarily on architectural contracts (boundary isolation and gate-verified merge contracts), not only on agent count or model capability.
The Square Law of Parallel Agent Collisions: Pair Growth, Zone Size, and Merge Cost
Potential collision pairs grow as n-squared; bounded zone size is what restores near-linear conflict growth
When many agents operate in the same mutable workspace, the number of potential collision pairs grows quadratically. That combinatorial fact does not by itself tell operators how to partition the team. This article keeps the square-law insight, then replaces an incorrect partition formula with a clearer tradeoff: within-zone collisions fall as zones get smaller, while cross-zone merge cost rises as zones get smaller. The optimal design usually comes from choosing a bounded zone size, not from a universal square-root law in the number of zones.