Most LLM planning research has been confined to discrete-state domains, where the set of possible actions is finite and enumerable. According to the new arXiv paper, existing planning frameworks typically treat planning as a search over that enumerable action space. That assumption breaks down in continuous-state domains, where actions are not a finite list.
The paper's title points to means-ends analysis as a remedy. Means-ends analysis is a classic problem-solving strategy that works by identifying the difference between the current state and the goal, then selecting actions that reduce that difference. The abstract suggests this approach helps LLMs plan in continuous-state settings, though the full method and results are not detailed in the abstract.
The preprint is announced as arXiv:2610.24928v1. Because the abstract is truncated, the article does not include experimental details or comparisons. The key contribution, based on the available text, is shifting LLM planning from enumerable action sets to continuous-state domains via means-ends analysis.