3 papers
cs.LG2026
Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability
Tao Tao, Maissam Barkeshli
We study the ability of Transformer models to learn sequences generated by Permuted Congruential Generators (PCGs), a widely used family of pseudo-random number generators (PRNGs).…
cs.CL2025
DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization
Tao Tao, Guanghui Zhu, Lang Guo +3
Prompt Optimization has emerged as a crucial approach due to its capabilities in steering Large Language Models to solve various tasks. However, current works mainly rely on the ra…
cs.LG2025
(How) Can Transformers Predict Pseudo-Random Numbers?
Tao Tao, Darshil Doshi, Dayal Singh Kalra +2
Transformers excel at discovering patterns in sequential data, yet their fundamental limitations and learning mechanisms remain crucial topics of investigation. In this paper, we s…