Semantic coherence is presented not as a quality metric for AI, but as a fundamental architectural property ensuring meaning remains stable and interpretable under pressure. Unlike current AI systems that approximate coherence through statistical pattern matching, a truly coherent system would possess an internal logic that makes semantic drift architecturally impossible. This requires a semantic nucleus, stable meaning representation, legitimate transition models, and pressure-resistant boundaries, treating meaning as a core primitive rather than an emergent behavior. AI
IMPACT This concept could redefine how AI systems are built, moving beyond statistical pattern matching to a more robust, semantically grounded architecture.
RANK_REASON The item discusses a theoretical concept related to AI architecture and meaning representation, presented as a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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