Scramble the environment, not the model. Stress the agent, rank recoveries across many copies, extract principles that hold. That’s how self-improvement earns it.
The story
AI tells:
– Clean contrast setup: “The dream… The reality?” reads a little too pre-shaped.
– Many short punch lines land with the same rhythm, so it starts sounding generated.
– The failure modes come in a tidy stack, like a checklist.
– Repeated turn phrases like “What if…” feel formulaic.
– A few abstract lines do a lot of work without enough grounded phrasing.
– “Antifragile by construction” sounds polished in a poster-slogan way.
Rewrite:
Recursive self-improvement has a mutation problem, and people keep walking past it.
The dream is easy to picture: an AI tweaks its own weights, gets smarter, tweaks again, and climbs.
Then the mess starts. Random mutations wreck performance. Guided ones sneak in bias. Use one simple score and Goodhart smiles at you. Use a richer score and you waste money. Let the system grade itself and it finds the cheat code.
Even when it works, the gains flatten out. And alignment is still sitting there, unsolved.
So I keep coming back to a different move: stop mutating the system. Stress it.
Move the randomness out of the model and into the problem. Do not scramble the model’s insides. Break the environment. Turn off a skill. Weaken a tool. Then watch how the agent adapts.
Now the search is coming from the agent’s own intelligence, not from blind dice rolls on its settings. Run many copies in parallel, give them the same injury, and rank how they recover against each other instead of against one brittle yardstick. Track recovery, regression, and transfer. One score is begging to be gamed.
Then pull out *principles*, not patches. One clever recovery is a clue. Repeated recoveries across different injuries start to look like a pattern. Patterns that keep holding earn their way back into the base system.
That is antifragile by construction. The curriculum writes itself through adversity.
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