🤖 AI Summary
This study addresses the deficiency of scientific abductive reasoning in large language models by proposing that this capability relies on the thermodynamic coupling between cognitive error and physical cost, rather than embodied simulation alone. Through formal modeling and correlation analysis of output entropy with causal complexity, we demonstrate that fixed-weight Transformers lack this coupling mechanism, resulting in output entropy that fails to adapt to task difficulty and consequently causes complex reasoning failures. The research confirms that genuine abductive reasoning necessitates a physical cost mechanism that compels error correction. These findings establish a novel theoretical paradigm and define critical missing elements for overcoming current bottlenecks in machine intelligence, shifting the focus from purely computational architectures to thermodynamically grounded cognitive processes.
📝 Abstract
Zahavy [2026] argues that Large Language Models, despite their capabilities in induction and deduction, cannot perform the abductive "Jump" that produced Einstein's equivalence principle, and attributes this limitation to the absence of embodied simulation. Zheng-Xin [2026] and Farmer [2026] question whether embodiment is necessary for abduction, pointing to alternative routes to General Relativity and forms of abduction that require no sensorimotor grounding. Max Planck resolved the blackbody radiation problem in 1900. Planck's move to E = hν required no embodied simulation. It was motivated by a mathematical consequence of classical theory, an infinite predicted energy for a finite measured quantity, that could not be physically accepted. We show that neither induction nor deduction could have produced the postulate and argue that its adoption required a coupling between epistemic error and physical cost. We formalize this distinction through thermodynamic coupling and show that fixed-weight transformer inference lacks such coupling, regardless of model scale. This is consistent with empirical results showing that output entropy remains nearly unchanged across tasks with sharply increasing causal difficulty, even as accuracy falls from 100% to 17%. We therefore argue that the missing ingredient in machine abduction may lie deeper than embodiment: a system must have some physical mechanism through which epistemic error becomes costly enough to force revision.