← Back to all news
arXiv Machine Learning By Giorgio Angelotti, Stephen Parsons, Federica Nicolardi, Youssef Nader, Sean Johnson, David Josey, Paul Henderson, Hendrik Schilling, Johannes Rudolph, Forrest McDonald, Elian Rafael Dal Pr\'a, Paul Tafforeau, Alessandro Mirone, Clifford Seth Parker, Jan Paul Posma, Benjamin Kyles, Claudio Vergara, Alessia Lavorante, Rossella Villa, Maria Chiara Robustelli, Marzia D'Angelo, Gianluca Del Mastro, Michael McOsker, Kilian Fleischer, Christy Chapman, Nat Friedman, William Brent Seales

Complete virtual unwrapping and reading of a rolled Herculaneum papyrus

Read the original on arXiv Machine Learning →

arXiv:2606. 29085v1 Announce Type: cross Abstract: The carbonized papyri from Herculaneum preserve the only large-scale library to survive from classical antiquity, but many unopened rolls remain unread because physical opening risks irreversible damage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computation and Language
Sep 22

Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts

Apollo Restore is a 24‑billion‑parameter large language model fine‑tuned from Mistral Small to fill in gaps in fragmentary Ancient Greek texts using a fill‑in‑the‑middle objective. It achieves state‑of‑the‑art performance on short lacunae, placing the correct restoration among its top twenty candidates for 80.6% of documentary‑papyrus, 54.6% of literary‑papyrus, and 61.0% of stone‑inscription gaps, outperforming previous models by significant margins. In blind expert evaluations, 20 specialists preferred Apollo Restore over the strongest baseline and judged its performance at least as good as human restorations in 77% of cases, while also improving the published reading of the Vesuvius‑carbonised papyrus P.Herc. 1667.

By Hope McGovern, Anna Dolganov, Samuel Belkadi, Guillaume Kunsch, Dimitris Vlitas, David A. Smith
llmsfine-tuningsafety
More like this →
arXiv AI
Jul 7

PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark

arXiv:2607. 03006v1 Announce Type: cross Abstract: Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts: legible labels, prescribed aspect ratios, and -- above all -- abstaining from fabricated scientific figures.

By Tianyi Yang, Dawei Fu, Youpeng Wu, Zixun Kou, Linrui Chen, Ruobing Jiang, Zijian Wang, Qiang Li
multimodalbenchmarks
More like this →