LLMs can't jump
A position paper examining a fundamental limitation of large language models: their difficulty with abductive reasoning, a capacity central to genuine scientific invention.
I explore the fundamental nature of scientific invention, highlighting the critical gap between the ability of humans to create computational systems from physical intuition and current artificial intelligence capabilities. While modern Generative AI excels at pattern recognition (induction) and logical proofs (deduction), we argue it fundamentally lacks the capacity for "abduction"—the intuitive leap required to generate novel explanatory hypotheses.
Using Einstein’s formulation of General Relativity as a case study, we demonstrate that LLMs are structurally incapable of creating new foundational axioms, particularly when observational data is scarce. Ultimately, we propose that integrating physically consistent, multimodal world models is the key to bridging this divide and unlocking true artificial scientific invention.