The Drift Neural Network: A Neuro-Symbolic Cognitive Architecture for Autonomous Systems

Abstract

The dominant approach to machine intelligence scales one architecture, the autoregressive transformer. A transformer is a fixed matrix of learned weights that produces output by statistical continuation. Four of its limits are structural, and adding parameters does not remove them: it has no internal test for truth, its predictions do not themselves certify that an action satisfies a rule, it overwrites earlier competence when it learns a new task, and it grounds its outputs in correlation rather than mechanism. We present the Drift Neural Network, a neuro-symbolic cognitive architecture that joins four faculties in one closed loop. It grounds continuous sensing into the discrete terms it reasons over and holds that sensing within safe bounds. It derives causal mechanism by intervention rather than by fitting correlation. It retains earlier competence across tasks in a distributed associative memory. And its reasoning core is an explicit executable graph that rewrites its own structure, admitting each change only after a formal check of the exact computation that will run. Adaptation is verified construction rather than gradient descent on a fixed function. We report an audited, reproducible campaign at toy scale. Each faculty was validated against adversarial controls, and self-modifying graph-search agents compose into a coordinating system that improves beyond isolated instances; grounding, causal modelling, and retention memory were evaluated in separate benchmarks. A safety ablation keeps the verification gate active in every arm and isolates repair: all arms generate the same 58.8% rate of structurally invalid proposals, none admits an invalid change, and repair drives the residual presented to the gate to zero, cutting search evaluations by 28.2%. We diagnose the architecture's central obstacle, the construction of compositional structure, on which local search over a circuit plateaus and exact modular synthesis succeeds, given a decomposition. The evidence supports viability, not a finished intelligence. Scaling and autonomous problem decomposition remain open.