RAOA: Alternating-Operator Neural Computation with Programmable Radio Propagation

2026-10-01

AI Summary

This study investigates the feasibility of extending programmable radio propagation from a communication channel to a computational medium. It proposes the RAOA architecture, which for the first time integrates alternating operator recursive computation with programmable wireless propagation. By multiplexing control parameters to alternately execute problem updates and mixing operations, it achieves compositional deep computation through repeated traversals. Methodologically, discrete optimization, constraint propagation simulation, and pretrained model adaptation techniques are employed to construct a passive-phase free-space model. Experimental results demonstrate that this architecture effectively enhances the solution quality for discrete objectives while accurately approximating wireless propagation operators. Furthermore, in language model adaptation tasks, it attains performance comparable to that of multilayer perceptrons (MLPs).

Abstract

Can programmable radio propagation serve as computational depth rather than only as a communication channel or one-shot analog transform? We introduce the Radio Alternating Operator Ansatz (RAOA), a recurrent computing architecture that alternates an energy-derived problem update with a mixing update over a persistent latent state. Recomputing the problem field after each mix makes repeated passes compositional even when the same learned controls are reused across depth. We evaluate this idea through exact discrete optimization, constrained programmable-propagation simulation, and pretrained-model adaptation. On discrete objectives, repeated execution can improve solution quality without increasing the learned-control count, and the same formulation handles higher-order interactions directly. A passive phase-only free-space model further shows that the required operators can be approximated by programmable propagation while retaining useful downstream behavior despite realization error. When inserted as a zero-initialized residual adapter, RAOA adapts pretrained language models with WikiText performance close to a matched shallow MLP across three model families, while reasoning-task transfer remains model-dependent. Together, these results connect alternating-operator computation, programmable radio propagation, and neural adaptation within one recurrent framework. The RF realization evidence is simulation-based rather than a hardware demonstration.

Problem

• programmable radio propagation
• computational depth
• alternating-operator computation
• recurrent computing
• neural adaptation

Innovation

• Alternating-Operator Computation
• Programmable Radio Propagation
• Recurrent Architecture
• Residual Adapter
• Discrete Optimization