Tom Zahavy’s ICML 2026 position paper makes a sharp claim about where LLMs actually stop: they can induce and they can deduce, but they can’t abduce. Using Einstein’s 1952 letter to Maurice Solovine as the frame, Zahavy maps scientific discovery as a cycle — sense experience, an intuitive “jump” to axioms, then logical deduction from those axioms. LLMs, he argues, have mechanized the last part (formal proof, à la AlphaProof) and the statistical pattern-matching of induction, but the generative step — the abductive leap that produces a genuinely new axiom from scarce or absent data — is structurally out of reach. The case study is the equivalence principle: Einstein didn’t derive general relativity by compressing data, because Newtonian physics faced no empirical crisis (the one anomaly, Mercury’s perihelion, was explained away with the hypothetical planet Vulcan). With no error signal, “creativity as compression” has no gradient to push a system toward restructuring spacetime. The fix isn’t a bigger LLM: Zahavy proposes action-controllable, physically consistent world models — synthetic laboratories where an agent can intervene counterfactually, cut the elevator cable, and ground symbols in simulated sensation. It’s a position argument, not a proof — reviewers pushed the conclusion from “confirms” to “suggests” — but it’s a genuinely provocative frame for what “AI for science” can and cannot mechanize.
LLMs Can't Jump — Tom Zahavy
An ICML position paper argues LLMs have mastered induction and deduction but structurally lack abduction — the creative leap that generates new axioms.