🤖 AI Summary
This study investigates whether the resource cycling mechanism in Metabolic Multi-Agent Optimizers (MMAO) possesses framework-level explanatory power beyond metaphorical interpretation. By constructing an abstract state model that strips away domain-specific details while preserving the core resource accounting mechanism, the work leverages abstract modeling, dynamical analysis, and stability theory under mild bounded gain-and-expenditure assumptions to reveal, for the first time, the endogenous regulatory dynamics of MMAO at the architectural level. The analysis establishes the non-negativity and boundedness of key resource variables—such as private energy and public budget—and identifies three universal behavioral modes: contraction, reinvestment, and search reallocation. The generality of these modes is validated through both continuous and discrete instantiations, clearly distinguishing behaviors inherent to the metabolic feedback loop from those contingent on implementation specifics.
📝 Abstract
This paper studies the Metabolic Multi-Agent Optimizer (MMAO) at the framework level rather than at the implementation or benchmark level. The central question is whether the metabolic resource loop of private energy, communal budget, role drift, and lifecycle turnover has a framework-level interpretation beyond narrative metaphor. We introduce a generic MMAO state model that abstracts away domain-specific move operators while retaining the resource bookkeeping that defines the framework. Under mild bounded-gain and bounded-spending assumptions, we establish boundedness and nonnegativity properties for private energy, communal budget, role state, and active population size. We then characterize three endogenous behavioral regimes of the loop: contraction under sustained resource deficit, reinvestment under surplus communal accumulation, and search redistribution under heterogeneous marginal returns across agents or subgroups. The analysis is intentionally conservative. It does not claim global convergence of the full adaptive system, universal superiority over specialist optimizers, or a complete stationary characterization of the resulting process. Instead, it identifies which internal regulation properties are generic consequences of the loop and which remain implementation specific. A compact mechanism-validation package on representative continuous and discrete MMAO realizations provides supporting empirical evidence for this reading, but is not intended to replace a full benchmark study. The resulting contribution is therefore a bounded, regenerative, resource-regulated interpretation of MMAO, rather than a complete proof of all adaptive behaviors of the full algorithm family.