arXiv paper proposes hierarchical "levels, ticks, cascaded intelligence" architecture for long-horizon agents
The arXiv preprint (arXiv:2609.19519v1) argues that long-horizon language-model agents need a persistent harness rather than model-weight changes, and proposes a three-part hierarchical architecture—time-indexed levels that store bounded summaries, clocked ticks as autonomous action units, and cascaded intelligence that escalates to stronger models after review. The authors report a ten-day campaign where an agent using this design reproduced a published reinforcement-learning result while a human attended only once per day, preserved state across context resets, and showed behavior changes driven by stored knowledge without changing model weights.