{
 "id": "Metavolve-Labs/alexandria-aeternum-genesis",
 "owner": "Metavolve-Labs",
 "name": "alexandria-aeternum-genesis",
 "downloads": 287,
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 "last_modified": "2026-10-21T00:49:15.000Z",
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 "licence_name": null,
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 "gated": false,
 "tasks": [
  "text-to-image",
  "image-to-text",
  "image-classification",
  "image-feature-extraction",
  "visual-question-answering"
 ],
 "citation": "@article{macpherson2026cognitive,\n  author = {MacPherson, Tad},\n  title = {Cognitive Nutrition for Foundation Models: Empirical Validation of the\n           PEST Framework for Provenance-Verified, Semantically Dense Training Data},\n  year = {2026},\n  institution = {Metavolve Labs, Inc.},\n  note = {Pre-print. Companion dataset: Alexandria Aeternum 10K}\n}\n\n@article{macpherson2025entropy,\n  author = {MacPherson, Tad},\n  title = {The Entropy of Recursion: Provenance-Embedded Semantic Transport\n           as a Countermeasure to Model Collapse and Context Blindness},\n  year = {2025},\n  institution = {Metavolve Labs, Inc.},\n  doi = {10.5281/zenodo.18436975}\n}\n\n@article{macpherson2026density,\n  author = {MacPherson, Tad},\n  title = {The Density Imperative: How Semantic Curation Depth Determines\n           Vision-Language Model Capability},\n  year = {2026},\n  institution = {Metavolve Labs, Inc.},\n  doi = {10.5281/zenodo.18667735},\n  note = {Controlled ablation on Llama 3.2 11B Vision-Instruct using 9,081\n          artifacts from the Alexandria Aeternum dataset}\n}\n\n@dataset{alexandria_aeternum_genesis_2026,\n  author = {Metavolve Labs, Inc.},\n  title = {Alexandria Aeternum: Genesis 10K --- Complete v3.0 with C2PA Manifests (test certificate)},\n  year = {2026},\n  publisher = {Hugging Face},\n  version = {3.0},\n  url = {https://huggingface.co/datasets/Metavolve-Labs/alexandria-aeternum-genesis}\n}",
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}