Language models can't spark scientific revolutions, but world models might

Language models can't spark scientific revolutions, but world models might Maximilian Schreiner View the LinkedIn Profile of Maximilian Schreiner Jul 30, 2026 Nano Banana Pro prompted by THE DECODER Can language models spark a scientific revolution? In a position paper titled "LLMs can't jump," Google Deepmind's Tom Zahavy argues they can't. They're missing the cognitive mechanism needed to create something truly new. Zahavy builds his case on a framework Albert Einstein sketched in a letter to his friend Maurice Solovine. Discovery, Einstein wrote, is a cycle: sensory experience leads to an intuitive "leap" toward axioms, and from there, logical deduction produces testable conclusions. Axioms are the unproven foundational assumptions of a theory. AI handles two of three types of reasoning To pinpoint where the gap lies, Zahavy draws on a classic distinction from philosopher Charles Sanders Peirce, who categorized all reasoning by how it connects rules, cases, and results. Deduction derives guaranteed conclusions from fixed rules, like running a program that produces a provably correct output. Induction spots patterns in data: observe a thousand white swans, and you generalize that all swans are white. Abduction is the creative leap. It invents a cause to explain a surprising phenomenon. This third form is where Zahavy sees the critical bottleneck, and he draws a line between two levels of it. Ordinary abduction picks the most plausible explanation from a set of known candidates, the way a doctor matches symptoms to a disease. Language models can do this, he concedes. The harder version is what he calls "manipulative abduction": inventing a cause for which no linguistic template exists yet. That, he argues, is the real bottleneck of scientific invention, and machines can't do it. Induction and deduction, the paper argues, are well within reach. Language models already excel at statistical pattern recognition, and they're rapidly conquering formal derivation too. Systems like AlphaProof, Gemini, and GPT-5 now achieve gold-level scores on International Mathematical Olympiad problems. Zahavy even concedes that a language model could probably derive general relativity if given Einstein's assumptions as a starting point. But formulating those assumptions in the first place, making the manipulative leap to reach them, remains the bottleneck. Why machines struggle with this leap, Zahavy illustrates using that very theory: AI models typically learn by comparing their predictions to reality and adjusting based on the error, the gap between prediction and outcome. Without a detectable error, there's nothing for the system to work with. And that's the situation Einstein faced, Zahavy argues. When Einstein was working, there was no data crisis. Newton's physics had been confirmed with extreme precision. The only known anomaly, a tiny shift in Mercury's orbit, had been attributed to a hypothetical hidden planet called "Vulcan." An optimization-driven AI would have had no reason to overthrow physics, Zahavy argues. Following the logic of the argument, it would have done what the astronomers of the era did: invented an extra planet to account for the small discrepancy, rather than rethinking space and time. The data confirming Einstein's theory, such as Eddington's measurement of light deflection, didn't arrive until years after the theory was formulated. A jump requires a body So where did the manipulative abduction come from that led Einstein to his axioms? Zahavy points to Einstein's "happiest thought": the freely falling observer who no longer feels gravity. This insight came from embodied simulation, Einstein mentally playing through a physical sensation rather than grinding through equations. He imagined a physicist inside an accelerating elevator in