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what if we apply recursion not to numbers or paths, but to abstract ideas?
This post is an attempt to think about idea generation as a constrained state-space search, inspired by recursion in computer science.
Read this blog : blog.eshanstudio.com/posts/abstract-recursion
https://arxiv.org/abs/2512.24601
You’ve set up artificial selection in idea-space with yourself as the designer. What if AI could learn the rules of coherence from examples instead?
You’ve set up artificial selection in idea-space with yourself as the designer. What if AI could learn the rules of coherence from examples instead?
Even if coherence is learned implicitly, choosing a language, model, or representation already defines a bounded idea-space. The bias doesn’t disappear, it just moves into data and architecture. So my question isn’t whether coherence should be explicit or learned, but whether absolute exploration of an abstract idea-space is possible at all once any boundaries are imposed.
Earlier quoted context omitted.
Even if coherence is learned implicitly, choosing a language, model, or representation already defines a bounded idea-space. The bias doesn’t disappear, it just moves into data and architecture. So my question isn’t whether coherence should be explicit or learned, but whether absolute exploration of an abstract idea-space is possible at all once any boundaries are imposed.
Guess absolute exploration hits the heat-death limit. You are hinting at a Drake-equation for bounded idea-space to guide AI: anchors x pressures x connectors x depth. Shift the boundaries for novelty.