Dimensions overall score 8.0

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• Meta Context Engineering (MCE) achieves 5.6-53.8% relative improvement over state-of-the-art agentic Context Engineering methods.result 90%
• MCE introduces a bi-level framework that co-evolves context engineering skills and context artifacts.method 95%
• Current Context Engineering methods impose structural biases and restrict optimization to a narrow design space.limitation 85%
• MCE maintains superior context adaptability, transferability, and efficiency in both context usage and training.result 80%
• MCE was evaluated across five disparate domains under both offline and online settings.method 90%
• The meta-level agent refines engineering skills via agentic crossover, a deliberative search over skill history.technical 85%
• MCE could replace traditional static context optimization methods that limit flexibility and performance.market 75%
• Practical implementation requires careful handling of intellectual property when using proprietary models.limitation 80%

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