Toward a Memory That Does Not Burn: A Proposed AI-Driven Federated Framework for Knowledge Recovery and Preservation

Knowledge is being lost quietly and permanently. Abandoned university servers, undigitized handwritten research notes, defunct institutional websites, and orphaned digital archives are disappearing dailywith no coordinated effort to stop it. Existing systems including the Internet Archive, HathiTrust,JSTOR, and Europeana address pieces of this problem but remain fragmented, passive, and siloed.None of them rescue, digitize, intelligently link, and preserve knowledge within a single unified framework.This problem is not new. In 1945, Vannevar Bush warned in his landmark essay As We May Thinkthat humanity was producing knowledge faster than it could organize or recover it. He proposed theMemex ,a system that would store, link, and navigate knowledge by association the way humanmemory naturally works. He had the vision but not the tools: no artificial intelligence, no distributedcomputing, no natural language processing, no optical character recognition at scale. The technologysimply did not exist.Eight decades later it does. This paper proposes an AI-driven federated framework for knowledgerecovery and preservation combining: distributed preservation nodes, an intelligent salvage layer, anOCR and handwriting recognition pipeline with human-in-the-loop correction, an AI-driven knowledgegraph for automatic association and exploration, and a personal knowledge layer giving each researchera persistent learning memory across recovered instituti