3 papers
cs.AI2026
Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
Maximo Eduardo Rulli, Thomas Vaitses Fontanari, Simone Petruzzi +9
Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches, DLMs are not explicitly cond…
cs.LG2026
Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
Gizem Yüce, Giorgos Nikolaou, Nicolas Flammarion
Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities. For autoregressive language models, not all tokens in a forget…
cs.LG2025
Language Models are Injective and Hence Invertible
Giorgos Nikolaou, Tommaso Mencattini, Donato Crisostomi +3
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 exac…