Summary
This discussion questions why Large Language Models (LLMs) perform reasoning using natural language, such as chain-of-thought, when their internal operations are fundamentally vector-based. It proposes exploring models that reason more explicitly within their latent/vector space instead of generating intermediate natural language steps. This theoretical inquiry could open new avenues for more efficient or robust LLM reasoning architectures.
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