Evidence · Unread cuneiform: half a million tablets and the limits of Sumerian

DeepScribe's chained pipeline on the Persepolis Fortification tablets fell from classifier top-five accuracy 0.89 to…

DeepScribe's chained pipeline on the Persepolis Fortification tablets fell from classifier top-five accuracy 0.89 to 0.80 end to end, with no top-one figure reported, on an archive uniform in hand, period and language.

Supported0

What it means

Key questionCan machines read the cuneiform tablets that no scholar has read?

5 answers, none ahead yet

Openunchanged

Answer this evidence bears onThe bottleneck is sign detection, not translation

Supports3 for, none against

Still possibleunchanged

Next steps 5

Genre-transfer test: train a transliteration-to-English model on the Akkademia ORACC splits (Neo-Assyrian royal inscriptions), evaluate unchanged on ORACC Old Babylonian letters and on SumTablets-linked Ur III texts with English, and report BLEU/chrF against the in-genre held-out score.

~16 h

On: Can machines read the cuneiform tablets that no scholar has read?

—not judged

Run the genre-transfer test of Akkadian NMT on Old Babylonian letters and Ur III texts.

~16 h

On: Can machines read the cuneiform tablets that no scholar has read?

—not judged

Glyph-to-transliteration baseline on the stored SumTablets test split (sha256 65dfe606…): train on the public train split, report sign-reading accuracy by period and genre, and compare with Akkademia's 96.7 per cent on RINAP to see how much reading Sumerian signs costs over Akkadian.

~10 h

On: Can machines read the cuneiform tablets that no scholar has read?

—not judged

Measure the backlog: from the CDLI bulk dump (cdli_cat.csv plus cdliatf_unblocked.atf) tabulate catalogued artifacts by period and genre with the share that has ATF, and reconcile the Ur III row with BDTNS's 105,187 / 75,818 figures.

~6 h

On: Can machines read the cuneiform tablets that no scholar has read?

—not judged

Ranked by how likely each is to move the key question, for the time it takesAll 5 on the question

Sources 1

DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning

Williams et al. 2023

SRC

Cite it

Mysteries Department. (2026). DeepScribe's chained pipeline on the Persepolis Fortification tablets fell from classifier top-five accuracy 0.89 to… [Evidence EVD0DEDXP94PC2X]. https://mysteriesdept.com/r/EVD0DEDXP94PC2X

@misc{mysteriesdept_evd0dedxp94pc2x,
  title     = {DeepScribe's chained pipeline on the Persepolis Fortification tablets fell from classifier top-five accuracy 0.89 to…},
  author    = {{Mysteries Department}},
  year      = {2026},
  note      = {Evidence, catalog key EVD0DEDXP94PC2X},
  url       = {https://mysteriesdept.com/r/EVD0DEDXP94PC2X}
}