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    <doi_batch_id>jabran-WORK-000003-1788537319920</doi_batch_id>
    <timestamp>1788537319920</timestamp>
    <depositor>
      <depositor_name>Chaudry, Jabran I.</depositor_name>
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    <registrant>Jabran.com</registrant>
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      <group_title>Treatise</group_title>
      <contributors>
        <person_name sequence="first" contributor_role="author">
          <given_name>Jabran I.</given_name>
          <surname>Chaudry</surname>
          <ORCID authenticated="false">https://orcid.org/0009-0008-1563-9401</ORCID>

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      </contributors>
      <titles>
        <title>The Architecture of Relevance</title>
      </titles>
      <posted_date>
        <month>08</month>
        <day>04</day>
        <year>2026</year>
      </posted_date>
      <institution>
        <institution_name>Jabran.com</institution_name>
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      <jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>Intelligence does not become wisdom by generating more possibilities. It becomes wisdom only when relevance is fixed under value, consequence, and answerability. This work argues that relevance is the hidden architecture beneath intelligence, ethics, governance, and artificial intelligence.

A system may detect relations, produce explanations, retrieve information, rank outputs, predict futures, and generate plausible structure; yet none of these capacities, by themselves, determine what matters. Relevance is not a property of information alone. It is the justified priority of a relation under a horizon of value, consequence, and answerability.

The age of artificial intelligence intensifies the problem because machines can now generate relations faster than human institutions can evaluate them. The danger is not only machine error. It is also machine abundance: too many plausible connections, too many summaries, too many rankings, too many futures, too many recommendations, and too many technically correct answers inside malformed frames.

Earlier theories of relevance have explained relevance in cognition, communication, inference, attention, or information processing. This work asks a different question: when intelligence can generate unlimited plausible relations, what makes any relation worthy of attention, trust, action, or institutional force?

The work develops Relevance Ethics as the study of how minds, institutions, and machines decide what matters under conditions of excess possibility. It distinguishes relation from relevance, salience from warrant, attention from justification, auditability from answerability, and correctness from judgment. It argues that artificial intelligence cannot be governed only at the level of outputs, because outputs are late-stage surfaces of prior relevance architectures. To govern intelligence is first to govern salience.

The future will not belong to those who know everything, but to those who can still decide what matters without surrendering answerability for the decision.</jats:p></jats:abstract>
      <item_number item_number_type="corpus_work_id">WORK-000003</item_number>
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