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A multi-agent framework that uses 'structured divergence'—calculating variance and consensus across multiple LLM outputs—to assist in decision-making and uncertainty quantification.
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This is a brand-new project (4 days old) with no stars or community traction. While the concept of using divergence as a signal for uncertainty in LLMs is scientifically valid, the project currently exists as a personal prototype. It faces high risk because frontier labs are rapidly integrating multi-agent orchestration (e.g., OpenAI Swarm) and evaluation tools directly into their platforms.
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