Elvis Sar is right: putting compute power into the execution layer is more practical than stacking parameters—but how do we fill the gap when a weak model’s equipment/module activation fails?

Beating monitoring shows that intelligent agents achieve self-evolution through updating external equipment.
Research decouples evolution into two dimensions: equipment updates and equipment benefits, finding that equipment updates make the underlying capabilities more uniform, with only a 3.1% difference in benefits between models.
The 9B Qwen 3.5-9B update skills are roughly equivalent to Claude Opus 4.6, implying that evolution can be completed using low-cost models.
Equipment benefits are non-monotonic; top-tier models approach a ceiling, weak models have significant room for improvement but yield less benefit, and are prone to "equipment activation failure" and "equipment compliance failure."
Elvis Sar echoes this, suggesting that computational power should be directed toward executing intelligent agents, strengthening autonomous equipment awakening and long-range instruction following.