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cs.CL2026
Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs
Pavel Tikhonov, Anton Korznikov, Matvey Mikhalchuk +6
While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams…
cs.CL2025
SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings and Speaks in Tokens
Nikita Dragunov, Temurbek Rahmatullaev, Elizaveta Goncharova +5
The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion object…
cs.CL2024
Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev +2
Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately mode…