3 papers
cs.CL2026
Semantic Chunking and the Entropy of Natural Language
Weishun Zhong, Doron Sivan, Tankut Can +2
The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern large language models (LLMs) have only recently approached. Thi…
cond-mat.dis-nn2025
Statistical Mechanics of Semantic Compression
Tankut Can
The basic problem of semantic compression is to minimize the length of a message while preserving its meaning. This differs from classical notions of compression in that the distor…
cond-mat.stat-mech2024
Random Tree Model of Meaningful Memory
Weishun Zhong, Tankut Can, Antonis Georgiou +3
Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across dif…