LLMs win gold at IMO but fail in medicine; data is key
balazskegl · x · 2026-08-18
LLMs have achieved gold medals at the IMO by verifying symbol strings but cannot determine treatment efficacy. Science lacks a mechanical verifier because its object is the world, requiring inductive and abductive reasoning on causal graphs—structures text underdetermines. Web-scale pretraining predicts text, not causal models.
Healthcare offers the missing signal: pairing human-curated concepts (pathologies, symptoms) with free text and intervention outcomes. This pairing can ground language in a causal model of the world. The author joined @Doctolib to build AI that helps clinicians and solve this grounding problem, questioning if major discoveries will come from LLMs with the right data or require a new architecture.
Related event: Ex-Kaggle CTO: LLMs Win IMO Gold but Can't Judge Medical Efficacy(2 posts)→
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