When the Canonical Completion Is Wrong: Formalizing and Measuring the Jump in Large Language Models

תקציר מקורי באנגליתarXiv:2608.26187v2 Announce Type: replace-cross Abstract: Whether large language models (LLMs) can perform the abductive leap from evidence to a new system of axioms, commonly referred to as a jump, has recently attracted considerable debate. A prominent position holds that LLMs are structurally incapable of such jumps, while recent studies challenge both its mechanism and empirical evidence. One of the main reasons why the debate remains open is the lack of a formal definition of the jump and of algorithms that test whether a jump appears. In this paper, we attempt to develop a formal account of the jump in four steps and measure the second. These steps ask what the default completion of partial data is, when abandoning it is forced, whether the abandonment is correct, and how successive