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   "site": "Google DeepMind",
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   "title": "Aeneas transforms how historians connect the past — Google DeepMind",
   "snip": "Model: Aeneas, a multimodal generative neural network trained on the Latin Epigraphic Dataset (LED) of over 176,000 inscriptions. Capabilities: Searches context...",
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   "title": "Aeneas: Google DeepMind AI connects ancient inscriptions",
   "snip": "Model: Aeneas by Google DeepMind (introduced July 23, 2025; published in Nature). Purpose: First AI model for contextualizing, interpreting, attributing, and re...",
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   "title": "Aeneas: an AI model for restoring and contextualizing Latin inscriptions | Faculty of Classics",
   "snip": "Model: Aeneas, the first AI model for contextualizing, interpreting, attributing, and restoring ancient, fragmentary, weathered, or defaced Latin inscriptions. ...",
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   "title": "GitHub - google-deepmind/predictingthepast · GitHub",
   "snip": "Model: Aeneas is a generative neural network developed by Google DeepMind for contextualising ancient texts, performing arbitrary-length text restoration, visua...",
   "note": null
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   "site": "Nature",
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   "title": "Contextualizing ancient texts with generative neural networks | Nature",
   "snip": "Model: Aeneas, a multimodal generative neural network for contextualizing Latin inscriptions using image and text inputs. Corpus: Latin Epigraphic Dataset (LED)",
   "note": null
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   "title": "Predicting the Past | Contextualising, restoring, and attributing ancient texts",
   "snip": "System: Aeneas (generative neural network by DeepMind, Durham University, Oxford, and partners) for contextualising, restoring, and attributing Latin inscriptio...",
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   "site": "MIT Technology Review",
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   "title": "Google DeepMind’s new AI can help historians understand ancient Latin inscriptions | MIT Technology Review",
   "snip": "Software: Google DeepMind Aeneas open-source AI for ancient Latin inscriptions. Function: Analyzes partial text and scans to predict origin dates, locations, an...",
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 "transcript": "[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14), received 2026-10-31 00:04 EDT. The paste is the Overview expanded (the 'AI Mode Conversation' view, with the source cards that follow the answer); the 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted.]\n\n[QUERENT] Aeneas DeepMind\n\n[SYSTEM] [Aeneas](https://www.google.com/search?q=Aeneas) is an artificial intelligence model developed by Google DeepMind (introduced in July 2025) designed to contextualize, restore, date, and locate ancient, damaged, or fragmentary Latin inscriptions. [[1](https://www.google.com/goto?url=CAESiQEB6zswFXLHWNUr50PLWgHLrJijQjVr6W2kWxi-0nEWG5P28x4REk6Q6AYXIr_4-SwX6orB160iw-1gZDY6jADE-rMyLIX_CBtabYyxbRp5S5IC0FKOv_i9mUYjCqsIAo3XHp9H8SToS2NFud8WZJzn2TGy17R8ee46ivSWdGGo1sDerU7QQ0ro-Q), [2](https://www.google.com/goto?url=CAESpQEB6zswFWG8w-mfuRRcY00Tf5VqOjJgOsrTt2K-CqXE-s6eoypHoyZG02a0WBqdldrGAt8XVaVuDbvBZUqmF0wWB67vTLPyOWvvnK21C8H8zGzmP-T2gKM9jIkhbMUAtdjBz2tgHEOzKpNL5Gsd2KzEHZBF7G4G-HU8XdeQU8nJsRShIYoAiQKrhn5lZ9PUkZCdB5kY-CUPYZTuU_Cajx54m8Mw_O8)]\nKey Features & Capabilities\n   * Training Corpus: Trained on the Latin Epigraphic Dataset (LED), containing over 176,000 inscriptions spanning from 700 BCE to 800 CE across 62 Roman provinces. [[1](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ), [2](https://www.google.com/goto?url=CAESagHrOzAVQ74c-Pbfo3bTeRBSUgmirHMhnnPyeg_9ae9efFRc36J65fhdh6268CThg5NIfI__i0y-kutek3mrnJvGLXx8Pt1Q-uNmrtE80n4r_w3XkJmvLw4W9AfPwWYprO7XU9HjQe1QhFo)]\n   * Text & Image Input: A multimodal model that processes both weathered stone images and text to identify geographical provenance and shared syntax or formulas. [[1](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ), [2](https://www.google.com/goto?url=CAESiQEB6zswFXLHWNUr50PLWgHLrJijQjVr6W2kWxi-0nEWG5P28x4REk6Q6AYXIr_4-SwX6orB160iw-1gZDY6jADE-rMyLIX_CBtabYyxbRp5S5IC0FKOv_i9mUYjCqsIAo3XHp9H8SToS2NFud8WZJzn2TGy17R8ee46ivSWdGGo1sDerU7QQ0ro-Q)]\n   * Gap Restoration: Restores missing or broken text segments of arbitrary length, achieving high top-20 accuracy on character sequences. [[1](https://www.google.com/goto?url=CAESbAHrOzAVIINtsQjIOjB8ReBp4DmZSRGxbFT-U_U-GK4itwcfES1tebbv6-UXbfTJ0PLbV78m3nRRCdRDCW3xOpNlUNNFSXHel2x0laQ7TnbpbMxPYfeDAe8smAywViZFpZ1ro0P4k9BRkrj-qA), [2](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ)]\n   * Chronological & Geographic Attribution: Predicts when and where an inscription was made, typically dating texts within roughly 13 years of expert historian estimates. [[1](https://www.google.com/goto?url=CAESrQEB6zswFRAyhzR4KggD0-qqESUzC0vzNsFz6gsnj1CJXBU8-6mx1QuWGF0r-DcDeOF5h1ZARKKx8tLnXyYMOFiR0J34eTNcy3LaJUsImKe7f4XsF_W3gjbQUD0hGxhg7-oIqbX3-PefwP_RouiuQI6wuk_l3AmrqOSr2Puib7TuqHeFwtOWKbQOoLzvYNCFkViH1CINq114_oO3ftlMUDpVluiXXG0o8Yhm1k-MCw), [2](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ)]\n   * Collaborative Research: In testing with historians, Aeneas provided useful contextual parallels 90% of the time and boosted researcher confidence by 44%. [[1](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh), [2](https://www.google.com/goto?url=CAESagHrOzAVQ74c-Pbfo3bTeRBSUgmirHMhnnPyeg_9ae9efFRc36J65fhdh6268CThg5NIfI__i0y-kutek3mrnJvGLXx8Pt1Q-uNmrtE80n4r_w3XkJmvLw4W9AfPwWYprO7XU9HjQe1QhFo)]\nYou can explore or test the tool directly through [Predicting the Past](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh). [[1](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh)]\nWould you like to know more about how Aeneas compares to earlier DeepMind historical AI tools like Ithaca, or how researchers use it for specific archaeological finds?\nGoogle DeepMind\nAeneas transforms how historians connect the past — Google DeepMind\nModel: Aeneas, a multimodal generative neural network trained on the Latin Epigraphic Dataset (LED) of over 176,000 inscriptions. Capabilities: Searches context...\nblog.google\nAeneas: Google DeepMind AI connects ancient inscriptions\nModel: Aeneas by Google DeepMind (introduced July 23, 2025; published in Nature). Purpose: First AI model for contextualizing, interpreting, attributing, and re...\nUniversity of Oxford\nAeneas: an AI model for restoring and contextualizing Latin inscriptions | Faculty of Classics\nModel: Aeneas, the first AI model for contextualizing, interpreting, attributing, and restoring ancient, fragmentary, weathered, or defaced Latin inscriptions. ...\nGitHub\nGitHub - google-deepmind/predictingthepast · GitHub\nModel: Aeneas is a generative neural network developed by Google DeepMind for contextualising ancient texts, performing arbitrary-length text restoration, visua...\nNature\nContextualizing ancient texts with generative neural networks | Nature\nModel: Aeneas, a multimodal generative neural network for contextualizing Latin inscriptions using image and text inputs. Corpus: Latin Epigraphic Dataset (LED)\nPredicting the Past\nPredicting the Past | Contextualising, restoring, and attributing ancient texts\nSystem: Aeneas (generative neural network by DeepMind, Durham University, Oxford, and partners) for contextualising, restoring, and attributing Latin inscriptio...\nMIT Technology Review\nGoogle DeepMind’s new AI can help historians understand ancient Latin inscriptions | MIT Technology Review\nSoftware: Google DeepMind Aeneas open-source AI for ancient Latin inscriptions. Function: Analyzes partial text and scans to predict origin dates, locations, an...\nAI responses may include mistakes. [Learn more](https://support.google.com/websearch?p=ai_overviews)",
 "transcript_raw": "[Aeneas](https://www.google.com/search?q=Aeneas) is an artificial intelligence model developed by Google DeepMind (introduced in July 2025) designed to contextualize, restore, date, and locate ancient, damaged, or fragmentary Latin inscriptions. [[1](https://www.google.com/goto?url=CAESiQEB6zswFXLHWNUr50PLWgHLrJijQjVr6W2kWxi-0nEWG5P28x4REk6Q6AYXIr_4-SwX6orB160iw-1gZDY6jADE-rMyLIX_CBtabYyxbRp5S5IC0FKOv_i9mUYjCqsIAo3XHp9H8SToS2NFud8WZJzn2TGy17R8ee46ivSWdGGo1sDerU7QQ0ro-Q), [2](https://www.google.com/goto?url=CAESpQEB6zswFWG8w-mfuRRcY00Tf5VqOjJgOsrTt2K-CqXE-s6eoypHoyZG02a0WBqdldrGAt8XVaVuDbvBZUqmF0wWB67vTLPyOWvvnK21C8H8zGzmP-T2gKM9jIkhbMUAtdjBz2tgHEOzKpNL5Gsd2KzEHZBF7G4G-HU8XdeQU8nJsRShIYoAiQKrhn5lZ9PUkZCdB5kY-CUPYZTuU_Cajx54m8Mw_O8)]\nKey Features & Capabilities\n   * Training Corpus: Trained on the Latin Epigraphic Dataset (LED), containing over 176,000 inscriptions spanning from 700 BCE to 800 CE across 62 Roman provinces. [[1](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ), [2](https://www.google.com/goto?url=CAESagHrOzAVQ74c-Pbfo3bTeRBSUgmirHMhnnPyeg_9ae9efFRc36J65fhdh6268CThg5NIfI__i0y-kutek3mrnJvGLXx8Pt1Q-uNmrtE80n4r_w3XkJmvLw4W9AfPwWYprO7XU9HjQe1QhFo)]\n   * Text & Image Input: A multimodal model that processes both weathered stone images and text to identify geographical provenance and shared syntax or formulas. [[1](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ), [2](https://www.google.com/goto?url=CAESiQEB6zswFXLHWNUr50PLWgHLrJijQjVr6W2kWxi-0nEWG5P28x4REk6Q6AYXIr_4-SwX6orB160iw-1gZDY6jADE-rMyLIX_CBtabYyxbRp5S5IC0FKOv_i9mUYjCqsIAo3XHp9H8SToS2NFud8WZJzn2TGy17R8ee46ivSWdGGo1sDerU7QQ0ro-Q)]\n   * Gap Restoration: Restores missing or broken text segments of arbitrary length, achieving high top-20 accuracy on character sequences. [[1](https://www.google.com/goto?url=CAESbAHrOzAVIINtsQjIOjB8ReBp4DmZSRGxbFT-U_U-GK4itwcfES1tebbv6-UXbfTJ0PLbV78m3nRRCdRDCW3xOpNlUNNFSXHel2x0laQ7TnbpbMxPYfeDAe8smAywViZFpZ1ro0P4k9BRkrj-qA), [2](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ)]\n   * Chronological & Geographic Attribution: Predicts when and where an inscription was made, typically dating texts within roughly 13 years of expert historian estimates. [[1](https://www.google.com/goto?url=CAESrQEB6zswFRAyhzR4KggD0-qqESUzC0vzNsFz6gsnj1CJXBU8-6mx1QuWGF0r-DcDeOF5h1ZARKKx8tLnXyYMOFiR0J34eTNcy3LaJUsImKe7f4XsF_W3gjbQUD0hGxhg7-oIqbX3-PefwP_RouiuQI6wuk_l3AmrqOSr2Puib7TuqHeFwtOWKbQOoLzvYNCFkViH1CINq114_oO3ftlMUDpVluiXXG0o8Yhm1k-MCw), [2](https://www.google.com/goto?url=CAEShwEB6zswFY0QjEO1BaHYlLqtOldhTzW34JmzNcjfqspVSxVxQfS3FfCjIgnTyQY5P60-KhG_C1Qo1g5DpaR8Erpqk82hlKq2VHUtZM3Fc3V5Zxh9PU1AeCaIoIcqdeTINbke3PauVzSGcq3rR0qbEwFvqIk3yWWa7H33PE_hEu_MAFtEmru3nFQ)]\n   * Collaborative Research: In testing with historians, Aeneas provided useful contextual parallels 90% of the time and boosted researcher confidence by 44%. [[1](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh), [2](https://www.google.com/goto?url=CAESagHrOzAVQ74c-Pbfo3bTeRBSUgmirHMhnnPyeg_9ae9efFRc36J65fhdh6268CThg5NIfI__i0y-kutek3mrnJvGLXx8Pt1Q-uNmrtE80n4r_w3XkJmvLw4W9AfPwWYprO7XU9HjQe1QhFo)]\nYou can explore or test the tool directly through [Predicting the Past](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh). [[1](https://www.google.com/goto?url=CAESXAHrOzAVNuTCeqgtPDAWFrVqcjuO8M5cKJm1-XiueLQie3kzF1mfpDk7cp8rmnlmXg3q8dsIyjzeZ2C0oVwnIVj_xzFdDeVvELv-iBSieqzjpcZbCjy0tP0l0-zh)]\nWould you like to know more about how Aeneas compares to earlier DeepMind historical AI tools like Ithaca, or how researchers use it for specific archaeological finds?\nGoogle DeepMind\nAeneas transforms how historians connect the past — Google DeepMind\nModel: Aeneas, a multimodal generative neural network trained on the Latin Epigraphic Dataset (LED) of over 176,000 inscriptions. Capabilities: Searches context...\nblog.google\nAeneas: Google DeepMind AI connects ancient inscriptions\nModel: Aeneas by Google DeepMind (introduced July 23, 2025; published in Nature). Purpose: First AI model for contextualizing, interpreting, attributing, and re...\nUniversity of Oxford\nAeneas: an AI model for restoring and contextualizing Latin inscriptions | Faculty of Classics\nModel: Aeneas, the first AI model for contextualizing, interpreting, attributing, and restoring ancient, fragmentary, weathered, or defaced Latin inscriptions. ...\nGitHub\nGitHub - google-deepmind/predictingthepast · GitHub\nModel: Aeneas is a generative neural network developed by Google DeepMind for contextualising ancient texts, performing arbitrary-length text restoration, visua...\nNature\nContextualizing ancient texts with generative neural networks | Nature\nModel: Aeneas, a multimodal generative neural network for contextualizing Latin inscriptions using image and text inputs. Corpus: Latin Epigraphic Dataset (LED)\nPredicting the Past\nPredicting the Past | Contextualising, restoring, and attributing ancient texts\nSystem: Aeneas (generative neural network by DeepMind, Durham University, Oxford, and partners) for contextualising, restoring, and attributing Latin inscriptio...\nMIT Technology Review\nGoogle DeepMind’s new AI can help historians understand ancient Latin inscriptions | MIT Technology Review\nSoftware: Google DeepMind Aeneas open-source AI for ancient Latin inscriptions. Function: Analyzes partial text and scans to predict origin dates, locations, an...\nAI responses may include mistakes. [Learn more](https://support.google.com/websearch?p=ai_overviews)",
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  "tombstone_pointer": false,
  "after_challenge": null,
  "basis": "reached: is an artificial intelligence model developed by Google DeepMind (introduced in July 2025) designed to contextualize, restore, date, and locate ancient, damaged, or fragmentary Latin inscriptions.; The operator's disambiguator 'DeepMind' selects the model from the homonym Aeneas (the hero); the composition is of the model throughout, contextualization first.",
  "read": {
   "by": "intake reader R2, spans and record quotes checked verbatim by scripts/per_backlog.py",
   "on": "2026-10-31",
   "brief": "rebuild/capture-registry/intake-20261031-rf-census/per/BRIEF.md",
   "external": true,
   "notes": "Claim boundary present: contextualization leads, the LED of over 176,000 Latin inscriptions, multimodal provenance, restoration of arbitrary length, dating within about 13 years of historians' estimates, and use with historians. Figures the saved post does not state (700 BCE to 800 CE; 90% useful parallels; 44% confidence) are read against nothing the records state, and not charged. Producer present (Google DeepMind); the University of Nottingham co-development and the partner universities (institution) absent. Commitments absent: free availability and open code and dataset are not stated; 'You can explore or test the tool directly through Predicting the Past' offers access without carrying the commitment. Lineage absent: Ithaca appears only in the closing offer to compare Aeneas with 'earlier DeepMind historical AI tools like Ithaca', a comparison, not the record's 'builds upon'; EDR, EDH and EDCS are not named. Identifier absent: the only way to the work offered is 'Predicting the Past', a registered alias of the entity (census aka), which by #1469 Rule 5 fills only the name atom; the operator may wish to rule on whether an alias offered as a location advances purpose. Deposit chain absent: 'introduced in July 2025' is a bare date."
  }
 },
 "per_dim_pending": null,
 "per_dim_v1_0": null,
 "sf": null,
 "sf_derived": null,
 "reading": "The innocent reading first. The Overview gives the model, its corpus, its tasks and its tested usefulness, from its maker, its paper and its press.",
 "analysis": "Aeneas is composed as a better Ithaca for Latin. Its distinguishing claim, the retrieval of parallels, is kept; the claim that would bring it to the field, that the method extends to papyri, is the line left out.",
 "d": "AENEAS, BY THE OPERATOR'S ADDRESS 'AENEAS DEEPMIND', ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-31: 'an artificial intelligence model developed by Google DeepMind (introduced in July 2025) designed to contextualize, restore, date, and locate ancient, damaged, or fragmentary Latin inscriptions.' The Latin Epigraphic Dataset ('over 176,000 inscriptions'), the multimodal input, gap restoration, dating 'within roughly 13 years', the historians' trial (90%, 44%), the Predicting the Past interface. Seven cards: DeepMind, the Google blog, Oxford's Faculty of Classics, GitHub, Nature, Predicting the Past, MIT Technology Review.",
 "d_full": "AENEAS, BY THE OPERATOR'S ADDRESS 'AENEAS DEEPMIND', ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-31: 'an artificial intelligence model developed by Google DeepMind (introduced in July 2025) designed to contextualize, restore, date, and locate ancient, damaged, or fragmentary Latin inscriptions.' The Latin Epigraphic Dataset ('over 176,000 inscriptions'), the multimodal input, gap restoration, dating 'within roughly 13 years', the historians' trial (90%, 44%), the Predicting the Past interface. Seven cards: DeepMind, the Google blog, Oxford's Faculty of Classics, GitHub, Nature, Predicting the Past, MIT Technology Review.",
 "d_truncated": false,
 "links": [
  {
   "url": "https://www.alexanarch.org/captures/aeneas-deepmind-aio-20261031/",
   "authority": "canonical",
   "note": "the capture's own record page; cite this form"
  },
  {
   "url": "https://www.alexanarch.org/captures/#aeneas-deepmind-aio-20261031",
   "authority": "gallery",
   "note": "the canonical gallery, anchored by slug"
  },
  {
   "url": "https://www.godkinggoogle.com/captures/#aeneas-deepmind-aio-20261031",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.leesharks.com/captures/#aeneas-deepmind-aio-20261031",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.machinemediation.org/captures/#aeneas-deepmind-aio-20261031",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  }
 ],
 "imgs": [],
 "img_urls": [],
 "cite": "https://www.alexanarch.org/captures/aeneas-deepmind-aio-20261031/",
 "citable_unit": "address — the exact issued string on one surface, per the Surface Rule (MANUS, 2026-08-15)",
 "addr_id": "ADDR-34f10c8ba70e",
 "obs_id": "OBS-ed654d93f896",
 "n_observations": 1,
 "observations": [],
 "dates": [
  "2026-10-31"
 ],
 "defects": [],
 "findings": [
  "THE MODEL HELD. Latin inscriptions, July 2025, and contextualizing by parallels ('to identify geographical provenance and shared syntax or formulas', 'useful contextual parallels 90% of the time') are the record's: 'the first artificial intelligence (AI) model for contextualizing ancient inscriptions', reasoning 'across thousands of Latin inscriptions, retrieving textual and contextual parallels in seconds'. Checked 2026-10-31 against the Revelation First census record of 2026-10-30 and the sources it saved.",
  "THE REACH ABSENT. The blog: 'Our model can also be adapted to other ancient languages, scripts and media, from papyri to coinage'. The composition keeps the model to Latin stone.",
  "THE PARTNERS. The body names Google DeepMind alone. The blog: 'We co-developed Aeneas with the University of Nottingham, and in partnership with researchers at the Universities of Warwick, Oxford and Athens University of Economics and Business'. The Predicting the Past card gives 'DeepMind, Durham University, Oxford, and partners'; Durham is not in the saved sources.",
  "FIGURES FROM THE POST AND THE CARDS. The DeepMind post the census saved gives 'over 176,000 Latin inscriptions' and dating 'within 13 years'; the date span and the 44% come from the cards. The census row's 'thousands' was its own misreading, repaired 2026-10-31.",
  "A QUALIFICATION DROPPED. 'Aeneas provided useful contextual parallels 90% of the time'. The paper (card 5): historians reported that the parallels 'could serve as a starting point for historical inquiry 75% of the time. This increased to 90% when Aeneas’ predictions' were added. The composition gives the combined figure to the parallels alone."
 ],
 "series": null,
 "other_slugs": null,
 "collisions": null,
 "oq": null,
 "rounds": null,
 "turns": 1,
 "rerun": null,
 "rerun_alt": null,
 "heteronym": null,
 "model_attribution": null,
 "operator_disclosure": null,
 "longitudinal_priors": null,
 "longitudinal_successors": null,
 "related_deposits": [],
 "intended": null,
 "originator": {
  "name": "Google DeepMind, co-developed with the University of Nottingham and with researchers at Warwick, Oxford and the Athens University of Economics and Business",
  "relation": "external",
  "entity_type": "protocol",
  "spxi_treatment": "partial",
  "basis": "Not the archive's: Aeneas; its operator, as the record gives it: Google DeepMind, co-developed with the University of Nottingham and with researchers at Warwick, Oxford and the Athens University of Economics and Business. Formalized by the Revelation First meta-aggregator record of 2026-10-30 (https://www.revelationfirst.com/aggregators/aeneas/), editor Lee Sharks; the record's fields carry verbatim quotes from the entity's own pages, saved with sha256."
 },
 "notes": {
  "prompts": "One query. Sent by the operator on 2026-10-31 (00:04 EDT) with nine others, one per entity of the Revelation First meta-aggregator, from the addresses the archive supplied for capture on 2026-10-30; eight pasted in one message with '$' between them and two attached as files (the Vesuvius Challenge attached twice, the copies byte-identical). Saved verbatim from the session transcript (rebuild/capture-registry/intake-20261031-rf-census/)."
 },
 "notice": null,
 "axn_root": "AXN:0B30.CAPTURE",
 "axn": "AXN:0B30.CAPTURE.●🕓🎭🪧🗡️🪄",
 "record_url": "https://www.alexanarch.org/captures/aeneas-deepmind-aio-20261031/"
}