Different Language Models Learn Similar Number Representations

Arxiv research paper investigating how different language model architectures represent numerical concepts internally. The study reveals convergent learning patterns suggesting that LLMs independently discover similar numerical encoding schemes regardless of their design, indicating potential universal principles in how neural networks process quantitative information.