Multi18 [upd] Link

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.

| Method | Avg. Reward (norm.) | Constraint Violations (%) | Cross-domain Transfer Gain | |----------|---------------------|----------------------------|----------------------------| | Mono | 0.61 | 22.1% | — | | Multi5 | 0.73 | 15.4% | +0.07 | | HRL | 0.69 | 18.9% | +0.04 | | Multi18 | | 8.3% | +0.21 | multi18

Multi18: A Scalable Framework for Cross-Domain Multi-Agent Coordination in 18-Dimensional Constraint Spaces Removing the coordination graph (i

Multi18’s advantage was most pronounced in domains 14–18 (high regulatory strictness), where the arbiter prevented 94% of violations without aggressive reward shaping. Extensions to open-ended domains (e.g.

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.

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.