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Researcher Carter Church says GPT-6 Astra helped him produce a working reading of a cipher letter to French general Auguste de Marmont in about six hours of model execution. His account describes a substantial decoding workflow and offers materials others can inspect, but it is not an independently confirmed OpenAI result—and five symbols remain uncertain.
What the six-hour claim means
The claim comes from Church’s first-person write-up, “Breaking the Marmont Cipher 1809,” published September 18, 2026. He reports about six hours of GPT-6 Astra execution time. That figure describes model execution, not necessarily the full human effort involved in preparing the image, building the workflow, and checking its output.
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Coverage by 36Kr on October 2, 2026 and a Reb Babel roundup on October 3 repeats the claim through secondary reporting. Neither provides independent technical validation. The careful description is that Church reports a working reading produced with GPT-6 Astra’s help—not that the model alone solved the cipher or that every character is certain.
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Church says the letter dates to late March 1809, rather than 1807 as an earlier listing had it. He worked from a reproduction in a 1969 issue of the French journal Revue historique des Armées, not a newly scanned original. The image plate was 1,202 × 1,836 pixels; Church estimates it provided roughly 19 pixels per sign.
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In Church’s count, the plate contains a plain French opening followed by 24 rows of cipher: 1,300 cipher units formed from 155 distinct signs. A partial table assembled by French cryptology historian Daniel Tant supplied 33 known letter values, covering 435 of those units. The remaining symbols made this more than a straightforward substitution exercise.
How Church says the decipherment worked
The reported method combined manual preparation, known information, and computational search. Church describes the following workflow:
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- Transcribe the image. He entered the cipher signs from the journal plate, whose small reproduction made visually similar handwriting difficult to distinguish.
- Group likely glyph variants. Similar-looking handwritten forms were assessed as possible instances of the same sign, reducing the number of distinct symbols the solver needed to handle.
- Apply known values and a word clue. The partial table’s 33 letter values were used alongside the plaintext word “CONSEQUENT.”
- Search for remaining assignments. Church says the solver used simulated annealing and French three-, four-, and five-gram statistics drawn from works by Victor Hugo, Alexandre Dumas, and Marmont’s memoirs.
- Check candidates against the plate. Proposed values were compared with occurrences of the signs in the image rather than accepted solely because they made plausible French.
Church also reports rerunning the process after removing Napoleonic texts and Marmont’s memoirs from the language model; he says it recovered the same reading. This is a useful robustness check within his account, but it is not a controlled comparison of models or an independent replication.
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The proposed plaintext is a military briefing listing French and allied positions and troop strengths before Austria’s 1809 campaign. Church compares those details with Napoleon’s March 16, 1809 letter to Eugène, which directs him to send the same force dispositions to Marmont in cipher. As Church quotes the instruction: “You will send, in a ciphered letter and by an intelligent officer, these dispositions to General Marmont.” He identifies the source as Correspondance de Napoléon Ier, volume 18, page 358.
That comparison gives the proposed reading a historical cross-check: Church says the sequence of places and troop figures aligns with Napoleon’s instruction. It supports the plausibility of the reading, but the account does not establish that every glyph has been resolved beyond doubt.
How certain is the solution?
Church says 29 signs represent whole words and lists five unresolved signs with best guesses. That distinction matters: a coherent message and a strong match to a related historical instruction can coexist with ambiguity in individual symbols. The result is best treated as a working decipherment whose key claims and remaining uncertainties are visible, not as a definitive character-by-character reading.
Church’s page includes a key, transcription, row-by-row decoding, and scripts intended to verify and regenerate the reading offline. Those materials make the proposal inspectable by other readers. The sources available for this account do not show OpenAI independently confirming the result; the detailed claim remains Church’s report.
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It demonstrates one reported case in which an AI model contributed to a historical cipher workflow that combined image transcription, prior cryptographic information, language statistics, search, and historical cross-checking. It does not establish a general success rate, show that GPT-6 Astra can decode arbitrary historical ciphers, or provide a controlled comparison with other models or human cryptanalysts. The available accounts concern this single letter, not a benchmark.
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