The faithful transfer of contextually-embedded meaning continues to challenge contemporary machine translation (MT), particularly in the rendering of culture-bound terms--expressions or concepts rooted in specific languages or cultures, resisting direct linguistic transfer. Existing computational approaches to explicitating these terms have focused exclusively on in-text solutions, overlooking paratextual apparatus in the footnotes and endnotes employed by professional translators. In this paper, we formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for MT. We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai and evaluate LLMs with and without reasoning traces on choice and content of explicitation. Experiments across intrinsic prompting and agentic retrieval methods establish the difficulty of this task, with human evaluation showing that LLM-generated paratexts improve audience comprehension, though remain considerably less effective than translator-authored ones. Beyond model performance, statistical analysis reveals that even professional translators vary widely in their use of paratexts, suggesting that cultural mediation is inherently open-ended rather than prescriptive. Our findings demonstrate the potential of paratextual explicitation in advancing MT beyond linguistic equivalence, with promising extensions to monolingual explanation and personalized adaptation.

For almost 50 years, CSV has been the format of choice for tabular data. Given the ubiquity of CSV and the pervasive need to deal with CSV in real workflows -- where speed, accuracy, and fault tolerance is a must -- we decided to build a CSV reader that runs in parallel. We conducted extensive benchmarks of ParaText against 7 CSV readers and 5 binary readers. Please refer to our benchmarking whitepaper for more details. In our tests, ParaText can load a CSV file from a cold disk at a rate of 2.5 GB/second and 4.2 GB/second out-of-core from a warm disk.