arXiv:2603. 19954v2 Announce Type: replace Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2508. 07743v2 Announce Type: replace Abstract: While transformers excel in many settings, their application in the field of automated planning is limited.
arXiv:2607. 11760v1 Announce Type: new Abstract: A theoretical understanding of Transformers is crucial to better understand the capacities and limitations of large language models (LLMs).
arXiv:2603. 02238v2 Announce Type: replace Abstract: Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite training data.
arXiv:2602. 08857v2 Announce Type: replace-cross Abstract: Recent work has shown that the computations of Transformers can be simulated in the RASP family of programming languages.
arXiv:2607. 17710v1 Announce Type: new Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning.
arXiv:2604. 25800v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) has been shown to empirically improve Transformers' performance, and theoretically increase their expressivity to Turing completeness.