Synergetic Autonomous Intelligence: Three Axioms for Recursive Semi-Closed Learning
Dongxu Li
Abstract
We introduce Synergetic Autonomous Intelligence (SAI), a theory and design program for learning systems that form, preserve, revise, and compose relations while continuing to learn. SAI is built on three axioms: consequential distinctions must not become orphaned (U); identity is determined by future-faithful continuation under a declared contract (I); and unresolved defects are repaired by the least lawful local completion at the lowest admitted grade (L). We model a model, agent, tool, team, or environment boundary as a recursive semi-closed system with a Kernel-Ring representation, a Compile-Open-Participate cycle, and a typed residual comparing actual with claimed continuation. Under explicit finite or affine contracts, we establish relative consistency and conditional non-redundancy of U/I/L, characterize a contract-relative semantic quotient, construct an exact joint stochastic refinement, derive a constrained residual contraction, prove finite-horizon continued-service viability, and give an executed-intervention identification result with an observational-confounding boundary. We define GRC-Close as the generative and repair schema for this contract. The framework also gives finite contracts for action-effect-feedback loop re-entry, partial failure, duplicate-effect idempotency, replay, concurrent delivery, causal uncertainty, and threshold-radix state representation. KREIT, the Kernel-Ring Equilibrium Information Theory layer, treats information as a typed, resource-bounded distinction; the control layer authorizes, executes, audits, and updates actions across cloud-edge-end scales. These results establish a common contract-level vocabulary for representation, relation, memory, action, communication, explanation, coordination, and growth, while marking the assumptions and open boundaries required for later generalization.