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Reflection (artificial intelligence)
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<p>Reflection in <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a>, notably used in <a href="/facts/Large_language_model/WnogWVJY">large language models</a>, specifically in <a href="/facts/Reasoning_language_model/n4J9rNes">Reasoning Language Models (RLMs)</a>, is the ability for an artificial neural network to provide <a href="/facts/Bottom-up_and_top-down_design/MvtTkA3x">top-down</a> feedback to its input or previous layers, based on their outputs or subsequent layers. This process involves self-assessment and internal deliberation, aiming to enhance <a href="/facts/Automated_reasoning/x5EdkKoU">reasoning</a> accuracy, minimize errors (like <a href="/facts/Hallucination_(artificial_intelligence)/gwBoRg6p">hallucinations</a>), and increase <a href="/facts/AI_interpretability/dS566nb2">interpretability</a>. Reflection is a form of "test-time compute," where additional computational resources are used during inference. </p>