Large language models are becoming increasingly capable reasoners. They can identify patterns across huge datasets and derive complex conclusions from established premises. But genuine scientific breakthroughs often require something different: reframing the problem and proposing premises that did not previously exist.
Google DeepMind researcher Tom Zahavy separates scientific discovery into three modes of inference: induction finds patterns in data; deduction derives conclusions from rules; and abduction proposes a new explanatory framework for a surprising result.
Today's LLMs are strong at induction and rapidly improving at deduction. The paper argues that they still lack the abductive jump needed to originate new foundational hypotheses. Given Einstein's equivalence principle, a model might derive much of the later mathematics. The harder step is inventing that principle when observations are sparse.
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