How agentic LLMs can systematically explore hypothesis space: pattern mining, concept mapping, invention landscapes

NickPassig · x · 2026-09-29

The author argues AI will improve the world by cheaply exploring combinations, representations, relationships, and hypotheses humans lack the time or bandwidth to investigate. Using a deliberately unrelated pairing—aerodynamic stall/boundary-layer separation and affine transformations in projective geometry—he outlines three practical techniques with agentic LLMs:

Conclusion: intellectual searches once impractical due to time, breadth, and attention costs can now run in minutes or hours. He ends by asking Grok which schools actually teach AI-assisted invention and discovery.

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