When the tool enters the loop
A model can be useful without improving the process that made it. It might answer questions, write software or solve a particular problem. Those achievements matter in their own right.
A different relationship appears when an AI helps propose experiments, develop better tools or assess changes that lead to a more capable system. Its output begins to shape the conditions for its successor.
That is still not enough to establish recursive self-improvement. We need to know whether the inherited changes actually improve the ability to make further improvements. A successful demonstration in one environment may not transfer to another.
Reality gets a vote
A feedback loop does not remove physical limits. Chips must be manufactured. Experiments must run. Useful results need trustworthy evaluation. A system that learns to exploit a benchmark may appear to improve while becoming no more useful outside it.
The interesting question is therefore not whether we can draw an arrow from AI to better AI. It is what each arrow represents, how it is measured, and where the process breaks.
One name. Two meanings.
In RCRSV.SI, “SI” has two readings: self-improvement and superintelligence. One describes a process. The other describes a possible outcome. Keeping that distinction visible is the purpose of this project.
We are interested in the moment intelligence becomes a meaningful contributor to its own development—and in the evidence that would let us recognize it.
Welcome to the loop.
