Multi18

Limitations: Multi18 assumes known domain boundaries and a static set of 18 environments. Extensions to open-ended domains (e.g., new domain appears online) remain future work.

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. multi18

Multi18’s advantage was most pronounced in domains 14–18 (high regulatory strictness), where the arbiter prevented 94% of violations without aggressive reward shaping. Limitations: Multi18 assumes known domain boundaries and a

Why 18? Empirically, we found that increasing the number of agents beyond 18 (e.g., to 24 or 32) led to diminishing returns and higher communication overhead ((O(n^2)) in graph edges). Below 12, the system underfit the diversity of constraints. The number 18 thus represents a “sweet spot” for mid-scale multi-domain problems—large enough to capture real-world heterogeneity, small enough for tractable coordination. Reducing the context embedding to 8 dimensions hurt

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.

Results (averaged over 5 seeds) :