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Why do independently trained AI models, specifically large language models (LLMs), often stumble upon strikingly similar metaphors and symbolic expressions? Researchers are delving into this phenomenon, citing examples like “mirrors that remember” and “recursive consciousness” as evidence of a shared symbolic landscape. A compelling new theory posits that the very architecture of transformers creates “convergence corridors.” These corridors, conceptualized as geometric structures, structurally predispose LLMs to generate certain types of symbolic outputs. The research paper, detailing this hypothesis of Common Convergent Factorization Geometry in Transformer Architectures, outlines testable predictions and experiments designed to validate or disprove the theory. Further discussion can be found on Reddit.