Long-horizon stability in neural systems

Establish training and architectural mechanisms that ensure stable behavior of neural systems over long temporal horizons, with particular relevance to neural computer instances that aim for consistent, reproducible execution across extended tasks.

Background

The paper introduces Neural Computers (NCs) as systems that unify computation, memory, and I/O in a learned runtime state, and outlines requirements for their mature form, Completely Neural Computers (CNCs).

Achieving reliable, behavior-consistent operation over long time horizons is identified as critical for acceptance and governance of NCs. The authors note that despite theoretical results on computational power, maintaining stability over extended execution remains unresolved in practice, citing challenges such as drift and catastrophic forgetting.