I went through my chat logs with Claude, the following is a short summary of how the cipher was cracked. I'm omitting a number of failed attempts, this is the winning path only. Whenever I write "the languages" below, I'm referring to English, Latin and Ancient Greek, as indicated by the original author.
1) I asked Claude to compute the word length distributions of the cipher and evaluate whether these are compatible with the languages and how likely it is that the ciphertext word breaks correspond to actual word breaks. It produced some charts (example below) and estimated, that there is nothing weird with spacing and that the length distributions correspond to English and Greek better than to Latin, if we assume that on average it takes 1.8 ciphertext characters for one plaintext character.
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2) So I decided to go with ciphertext words as plaintext words and investigate multi-character substitution. I asked Claude to produce the frequency charts for prefixes and suffixes or the ciphertext and compare them to 10 most common words for each language. The result was inconclusive, so I decided that most likely we are dealing with a many to one scheme, where there are several ways of representing a single character. (I'm not sure if this was correct, probably @nablator can comment. My main focus was on investigating the usage of AI to break ciphers, not the specifics of this particular cipher.)
3) Then I asked Claude to produce top 100 short common word combinations for the languages (the likes of "this is", "most of", "has been", etc, for English, Latin and Greek), and filter out those that have no repeated letters ("it has", etc). Then I asked it to write some code to attempt identifying similar sequences in the code. Here I had to ask it to write some tests for the code, since Claude couldn't produce the right algorithm initially, but after a few attempts it succeeded.
4) After running this code Claude identified a few plausible ciphertext sequences corresponding to common word combinations. The top match was for some Greek, but Claude deduced that if the substitution is applied to the whole text, the result didn't look plausible.
5) The second top match was "it is" for "NO/T NO/S". I found it interesting that "/T" matches to "t" here and "/S" to "s", but even without this I would have tried to follow this match. So, I asked Claude to replace (by writing code) all "NO"s to "i" and all "/T" to "t" and all "/S" to "s", which produced the following text (beginning of the second block):
Code:
MTO iMRLONSMO LOP/ONPMO is LO MTO PLO/RMTONR/ONR LO TMO/PLRMO LTMOLTi/KLOtMOLT TO LOMTONRLO iR LOMTONRs it is LPGOiLRt OMP PMONRtMOLRi/K MRLO/RLP/LMO GKiMT i/RONR LONRLT GK/OOLTMONR 4S/OiRtS it is LO GTSLP/RiLT TMOR/PLRMO GOsiGR
This looked very promising, especially with "it is LO" and "is LO", so I asked Claude to adjust the mapping to add "LO" => "a".
Then I found two repeating short sequences: "aNRLT" and "a/RMO", so I guessed that one of them is probably "and" and the other "are", after using these mappings the whole second block turned to:
Code:
MTO iMRaNSe aP/ONPe is a MTO ParMTOn/On a Te/PLRe dedi/Kated TO aMTOna iR aMTOns it is LPGOiLRt OMP PenteLRi/K MRarLP/Le GKiMT irOn and GK/OOden 4S/OiRtS it is a GTSLPrid TeR/PLRe GOsiGR LP/OMT 4TO d/ORi/K ORder and MTO i/OnO/K ORder MTO OGOter KOLRGOMRns are d/ORi/K arRanSed iR MTO PeriMPOraR STGSLRe GOnLRi/Ke a TRaTiMSi/OnaR PeriMPOraR Te/PLRe 4TO 4PrOnt MP/eatGOres eiGKTt KOLRGOMRns iRsTeaT OMP Si/KS MTO 4ROtOPes de/Pi/KT a 4TiMPPOrent MRGSMT/OLR/ONSO/K/aR KOnMPLRi/KT On eatMS MPase Re/PResentiGR MTO NPO/KTORGS OMP ORder ONPer KGTa/OS MTO iRRer KOLRGOMRns are iR MTO i/OnO/K ORder MTO MPrieLSe de/Pi/KTS MTO PanaMTOnai/K PROSeSi/On a n/OTaPRe LPrea/K MPrOMR 4TO TGS/Pi/KaR MRGSMT/OLR/ONSO/K/aR SGOLPNSe/KTS KarNPed iRtO MR/OST TeR/PLRes MTO GTSLPrid natGOre OMP 4TO TeR/PLRe RePLRe/KTS aMTens KOnRe/KTi/On GKiMT LP/OMT MRaiRLROnd 4KreOse
And this is where you can already read parts of the message, the rest was very easy, but Claude and I got stuck trying to identify the exact workings of the cipher (nablator ≫ Claude), so after a few more attempts I just gave the result with roughly 80% of the plaintext revealed to ChatGPT and asked it to deduce the missing pieces.