Iterative convergence describes a process where repeated steps bring a system or calculation closer to a stable, desired outcome. In computational and algorithmic contexts, this refers to a sequence of approximations that progressively refine a solution until it meets a predefined criterion or reaches a fixed point. This method is common in numerical analysis, optimization problems, and certain consensus mechanisms within distributed systems. Each iteration adjusts the state based on previous results, moving towards a final, consistent state.
Context
Iterative convergence is a foundational concept in the design of various blockchain protocols, particularly in achieving consensus or settling disputes in decentralized networks. News might discuss how different consensus algorithms utilize iterative processes to finalize blocks or resolve forks. The efficiency and security of these iterative steps are critical for the overall performance and reliability of the underlying blockchain.
Applying BFT-secure Hashgraph to LLM ensembles creates a novel, iterative consensus protocol that formally verifies model outputs, dramatically boosting AI reliability.
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