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 hand
written research notes, defunct institutional websites, and orphaned digital archives are disappearing daily
with 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 Think
that humanity was producing knowledge faster than it could organize or recover it. He proposed the
Memex ,a system that would store, link, and navigate knowledge by association the way human
memory naturally works. He had the vision but not the tools: no artificial intelligence, no distributed
computing, no natural language processing, no optical character recognition at scale. The technology
simply did not exist.
Eight decades later it does. This paper proposes an AI-driven federated framework for knowledge
recovery and preservation combining: distributed preservation nodes, an intelligent salvage layer, an
OCR and handwriting recognition pipeline with human-in-the-loop correction, an AI-driven knowledge
graph for automatic association and exploration, and a personal knowledge layer giving each researcher
a persistent learning memory across recovered instituti