Multi18 ((full)) «UPDATED»

Results (averaged over 5 seeds) :

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. multi18

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. Results (averaged over 5 seeds) : Real-world AI

A. Chen, B. Novak, S. Kapoor Institute for Distributed Intelligence The design principle of choosing N based on