Author

Angelo Nardone

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Preprint Aug 2026

Diffuse to Compress: Leveraging Diffusion LMs for Lossless Compression

It is argued that replacing autoregressive LLMs with DLMs within the same compression framework could overcome the throughput bottleneck caused by their one-symbol-per-step limitation, and the newly proposed DLM-based framework advances the state of the art in lossless text compression.

Angelo Nardone, P. Ferragina · 0 citations

#large language models Jul 2026

LLM-based Source Code Compression via Thresholded Symbol Ranking

This work introduces LLM-based compressors deploying two novel symbol-ranking variants that bound predictions to the top-$T$ ranks, and conducts the first large-scale evaluation of LLM-based source code compression across 30 LLMs, including general-domain, code-specialized, and quantized models.

Angelo Nardone, P. Ferragina · arXiv.org · 0 citations