2026/02/20 by Ortal Hadad, Edoardo Loru, Jacopo Nudo +3 · 1 voice
Computer Science · Physics and Astronomy · Social Sciences · #Authorship Attribution and Profiling #Compression (physics) #Generative grammar #Generative model #Language and cultural evolution #Lossless compression #Measure (data warehouse) #Probabilistic logic #Scale (ratio) #Signature (topology) #Statistical model #Text Readability and Simplification #cs.CL #cs.CY #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2602.18152
published in Open MIND
openalex publication_date 2026/02/20 · arxiv published 2026/02/20 · arxiv updated 2026/02/20 · openalex created_date 2026/02/24 · openalex updated_date 2026/07/28
Large language models generate text through probabilistic sampling from high-dimensional distributions, yet how this process reshapes the structural statistical organization of language remains incompletely characterized. Here we show that lossless compression provides a simple, model-agnostic measure of statistical regularity that differentiates generative regimes directly from surface text. We analyze compression behavior across three progressively more complex information ecosystems: controlled human-LLM continuations, generative mediation of a knowledge infrastructure (Wikipedia vs. Grokipedia), and fully synthetic social interaction environments (Moltbook vs. Reddit). Across settings, compression reveals a persistent structural signature of probabilistic generation. In controlled and mediated contexts, LLM-produced language exhibits higher structural regularity and compressibility than human-written text, consistent with a concentration of output within highly recurrent statistical patterns. However, this signature shows scale dependence: in fragmented interaction environments the separation attenuates, suggesting a fundamental limit to surface-level distinguishability at small scales. This compressibility-based separation emerges consistently across models, tasks, and domains and can be observed directly from surface text without relying on model internals or semantic evaluation. Overall, our findings introduce a simple and robust framework for quantifying how generative systems reshape textual production, offering a structural perspective on the evolving complexity of communication.