One of the most well written paper I have ever came across. Apparently humanity still has hope, at-least until we build embodied agents that can function in physically realistic environment.
How do we fundamentally discover new things? In a letter to Maurice Solovine, Albert Einstein conceptualized discovery as a cyclical process involving an intuitive ‘jump’ from sensory experience to axioms, followed by logical deduction. While Generative AI has mastered Induction (statistical pattern matching) and is rapidly conquering Deduction (formal proof), we argue it lacks the mechanism for Abduction—the generation of novel explanatory hypotheses. Using Einstein’s formulation of General Relativity as a computational case study, we demonstrate that the prevailing theory of “creativity as data compression” (induction) fails to account for discoveries where observational data is scarce. This position paper argues that while a modern Large Language Model could plausibly execute the deductive phase of proving theorems from established premises, it is structurally incapable of the abductive ‘Jump’ required to formulate those premises. We identify the translation of simulation into formal axioms as the critical bottleneck in artificial scientific invention, and propose that physically consistent, multimodal world models offer the necessary sensory grounding to bridge this divide.
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.