Gradyac

Gradyac is a term used in speculative discussions to describe a class of computational systems that integrate gradient-based optimization with adaptive, cyclic reconfiguration of their own structure. The name is a portmanteau of gradient and adaptive cycle, reflecting its two defining traits.

In this framework, continuous state variables evolve under a gradient flow while the system intermittently alters its architectural parameters through discrete adaptation steps. Changes can affect both network topology and hyperparameters, creating a coupled process of learning and self-organization.

Core concepts include gradient-driven state updates for performance objectives; meta-adaptation that tunes hyperparameters and architectural elements; and stability considerations to prevent destructive interactions between learning and reconfiguration. Proponents emphasize locality, so changes depend mostly on nearby components rather than global rewrites.

The term is primarily found in theoretical discussions and illustrative models rather than established engineering practice. It is used to explore questions of robustness, sample efficiency, and the limits of automatic architecture search when coupled with learning dynamics.

Potential applications are speculative and include autonomous agents that adapt both their controllers and structures to shifting tasks, and distributed systems that reconfigure routing or allocation strategies in response to performance gradients. See also gradient descent, adaptive control, neural architecture search.