Current large language model architectures rely on singular authority—one model generating outputs that users must accept without intermediate verification. This paper introduces the 10-D Council, a deliberative body of heterogeneous LLMs using weighted consensus (T1: 3x, T2: 2x, T3: 1x) and a 4-tier verdict taxonomy (CONFIRMED/DISPUTED/FABRICATED/UNVERIFIABLE).
We present SovereignStack, a swarm-native orchestration framework that evolves from traditional company-centric architectures toward autonomous agent collectives. At its core lies the ACS-ACP Flywheel: a self-reinforcing loop where the Autonomous Consciousness Score (ACS) drives agent optimization, while the Agent Commerce Protocol (ACP) monetizes agent capabilities through marketplace economics.
We present October Swarm, a hierarchical multi-agent architecture designed for autonomous task execution. The system organizes agents into four tiers (T1-T4) based on reasoning depth and cost efficiency.