We introduced Multi18, a framework for multi-agent coordination across 18 distinct domains. By combining per-domain specialization with a global constraint-satisfaction layer, Multi18 outperforms monolithic and lower-agent-count baselines. The design principle of choosing N based on empirical complexity bounds (here, N=18) may generalize to other “multi-N” systems in applied AI.
Multi18: A Scalable Framework for Cross-Domain Multi-Agent Coordination in 18-Dimensional Constraint Spaces multi18
The “multi” prefix in AI often implies flexibility, but most multi-agent systems are tuned for 2–5 specific domains. We ask: Can a single architecture gracefully handle 18 qualitatively different environments without retraining? The number 18 arises naturally in certain industrial settings: 18 major languages, 18 time zones, 18 sub-components of a complex supply chain. We introduce Multi18—a proof-of-concept system where 18 specialized agents share a common communication protocol and a dynamic resource allocation mechanism. latency). We propose Multi18
Real-world AI systems increasingly operate across multiple domains (e.g., healthcare, finance, logistics) while adhering to diverse constraints (e.g., legal, ethical, latency). We propose Multi18 , a modular framework designed for environments characterized by exactly 18 distinct operational modalities. The framework combines a lightweight negotiation protocol among specialized agents, a shared latent space for cross-domain state representation, and a constraint-satisfaction layer. Initial experiments in 18 simulated environments (varying resource availability and regulatory strictness) show that Multi18 reduces task-switching overhead by 37% and improves constraint adherence by 28% compared to monolithic baselines. 18 time zones
Multi18’s advantage was most pronounced in domains 14–18 (high regulatory strictness), where the arbiter prevented 94% of violations without aggressive reward shaping.
Results (averaged over 5 seeds) :
Removing the coordination graph (i.e., independent agents) increased constraint violations to 27.4%, confirming the need for resource-aware arbitration. Reducing the context embedding to 8 dimensions hurt performance in the 10 text-based tasks (drop to 0.71 normalized reward), suggesting that 18 is a meaningful granularity for the tested diversity.