#ai This is more than language models. Somehow many forecasting models also almost fall inside the realm. Somehow the root of this is the latent space. Time series models even classical ones, have enlarged latent space, which is more or less embedding patterns with higher dimensions.However this paper is a bit fishy. I just can't trust the proof of theorem 2.2. Language Models are Injective and Hence Invertible

#ai

This is more than language models. Somehow many forecasting models also almost fall inside the realm. Somehow the root of this is the latent space. Time series models even classical ones, have enlarged latent space, which is more or less embedding patterns with higher dimensions.

However this paper is a bit fishy. I just can't trust the proof of theorem 2.2.

Language Models are Injective and Hence Invertible
https://arxiv.org/abs/2510.15511

arXiv.org

Language Models are Injective and Hence Invertible

Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exact recovery of...