Why recursive self-improvement hasn't happened: strategy and memory, and two fixes
TheTuringPost · x · 2026-09-07
Turing Post breaks down two bottlenecks blocking recursive self-improvement (RSI):
- Strategy: AI executes and iterates well but rarely updates the method driving its own improvement, falling out of the full RSI loop — a lack of "rethinking."
- Memory: Models already have the right skills, but they get buried in long, outdated context, causing agents to lose skills, retrieve them at the wrong moment, or miss key actions.
Two proposed solutions: Meta^n searches for better RSI strategies via a multilayered system, while Recuris evolves the external memory-control layer around a frozen model. Both feature inventive workflows, unpacked in the full breakdown.
Related event: Two New Approaches Aim to Unlock Recursive Self-Improvement in AI(2 posts)→
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