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This paper replicates the finding of 'emotion vectors' in open-weight LLMs Apertus-8B and Gemma-4-E4B, showing that valence geometry is recoverable across models with differences in layer emergence. The study also finds that arousal encoding is sensitive to the story corpus used for extraction.
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.