Why LLMs Can Invent: Compositional Reasoning and Asymmetric Verification
bindureddy · x · 2026-08-31
LLMs can solve untrained problems by composing known operators, leveraging "asymmetric verification" where proposing is hard but checking is cheap.
- Compositional Innovation: New problems are novel combinations of known blocks (e.g., math lemmas); models learn to reorder them to solve "unseen" issues.
- Search + Verify: Propose solutions via massive test-time search, then filter with a verifier to keep only the truth.
- Success Cases:
- AlphaProof won IMO Silver by searching proof space with Lean as a verifier.
- FunSearch discovered new math results on the cap set problem.
- AlphaFold generalizes to unseen proteins by learning physics, not lookup tables.
- Future Impact: Biology and energy follow the same pattern (massive search spaces + cheap simulation), offering hope for curing cancer and infinite energy.
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