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This document investigates the internal reasoning processes of large language models (LLMs), focusing on how they transform token-level information into abstract relational structures. The authors analyze the layer-wise organization of LLMs, finding that middle layers are primarily responsible for information composition and transformation, while outer layers preserve and route input-related features. Their findings suggest that abstract reasoning is not uniformly distributed across layers but is concentrat
- Author
- samuel chen
- Language
- EN