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:
- Pattern mining: search across vorticity, lift, drag, pressure, time, wall stress and other variables for recurring structures; may surface insights like stall being better modeled as a cascading state transition than a single threshold.
- Concept mapping: explicitly define concepts, dependencies, and causal structures grounded in verified sources, producing machine-readable causal graphs, ontologies, and dependency networks (O→P: AoA, geometry, Re, Mach).
- Problem and invention landscapes: decompose problems, find analogous mechanisms in other fields, generate and systematically evaluate cross-domain invention candidates, with digital experiments keeping the scientific method in play.
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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