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The author proposes a novel experimental framework to study identity formation in LLMs as hypergraph evolution through multi-instance interaction, distinguishing it from standard multi-agent debate by focusing on structural divergence in activation space rather than task performance.
This paper investigates reasoning in LLMs as an intrinsic dynamical process, finding that inference-time representations self-organize into low-dimensional manifolds. It proposes a label-free diagnostic based on internal dynamics to assess reasoning quality, suggesting that effective reasoning is governed by geometric and informational constraints.