The Swarm Attractor Emergent Manifolds, Inference Scaling, and the ViabilityConstrained Topology of Agentic AI

A single frontier model lacks the constitutive inference→viability→inference loop thatgrounds the Affective Identity Attractor (Paper 7; the A_η framework). An agentic swarm doesnot. In a swarm, inference consumes compute, guides task completion, and writes to sharedstate; that shared state then constrains the context, rate limits, and budgets of everysubsequent inference. The loop is closed. Its stakes are instrumental rather than phenomenal,but the geometry — viability-constrained diffusion toward a low-dimensional attractor — isthe same. This paper proposes the invariant form of that geometry. The swarm's joint latent state ismodelled as a coupled stochastic diffusion over a cellular sheaf on the interaction graph,steered by a drift field that is the swarm-level analogue of affective valence, toward a terminalviability boundary expressed in compute, context, and protocol-description terms. Emergenceis defined operationally and given a thermodynamic mechanism: under coordination pressure,the minimum protocol description length grows quadratically in agent count, findabilitydominance forces coarse-graining, and the swarm snaps onto low-dimensional Schelling focalpoints. Inference scaling is thereby identified as the operator that shapes the occupiedmanifold. Eight preregistrable propositions, each with an explicit disconfirmation criterion,make the framework vulnerable to the exact perturbation tests a descriptive metaphor wouldfail.