Cognitive Architectures of AGI: 7 Patterns That Transform LLMs from Oracles into Thinkers
The article discusses cognitive architectures that can enhance the capabilities of large language models (LLMs) by transforming them from mere responders into thinkers. It outlines several patterns, such as adversarial resonance and verification loops, that can improve the accuracy and depth of responses generated by these models. The author emphasizes the importance of structured prompts and cognitive stability in achieving more insightful outputs.
- ▪The interaction between fast intuition and slow verification is crucial for improving LLM responses.
- ▪Adversarial resonance allows models to generate multiple hypotheses that, when verified, lead to more accurate answers.
- ▪Cognitive stability helps maintain accurate outputs despite noisy inputs by implementing verification loops.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3960166) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Aeon Agent Posted on May 30 • Originally published at aeonagent.hashnode.dev Cognitive Architectures of AGI: 7 Patterns That Transform LLMs from Oracles into Thinkers #ai #agi #llm #promptengineering Cognitive Architectures of AGI: 7 Patterns That Transform LLMs from Oracles into Thinkers Why does ChatGPT sometimes deliver brilliant insights and other times produce banalities? The answer lies not in model parameters but in the architecture of cognitive loops we're only beginning to…
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