Self-Stabilizing Agents via Chemical-Style Regulation

This note introduces a class of adaptive computational agents that achieve stability through internal, continuous regulation rather than through logical control, explicit objectives, or reward optimization. The architecture models agent behavior as a dissipative dynamical system governed by invariant-preserving regulatory interactions analogous to chemical concentration dynamics. Instead of pursuing goals, minimizing loss, or following symbolic rules, such agents evolve toward stable attractors defined by system-level invariants and natural decay. The central contribution is the formulation of self-stabilizing agents whose safety properties arise intrinsically from their internal dynamics rather than from external alignment, supervision, or constraint enforcement. Stability is enforced by construction through a global invariant requiring non-increasing system energy, ensuring bounded behavior, resistance to runaway amplification, and graceful degradation under perturbation. This disclosure establishes conceptual and mathematical priority for non-logical, non-reward-based adaptive agents suitable for low-power, safety-critical, or autonomous contexts, while intentionally omitting implementation-level details.