Persona vectors do not emerge as a single capability; each persona has its own onset and ceiling ordered by prevalence and behavioral simplicity in pretraining data

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• When do persona vectors first emerge during model training?question0.856
  First research question driving the study of early persona vector formation
• Persona vectors are present from the earliest available Apertus-8B checkpoint at 1.4% of pretraining (210B tokens)finding0.836
  Replication finding on Apertus confirming early persona formation generalizes across model families
• Hypothesis 1 (Gateway Features): Persona vectors act as gateway features—single directions in activation space that shape LLM behavior across most, if not all, contextshypothesis0.825
  First of three hypotheses about persona implementation in LLMs, motivating the persona views
• Persona vectors capture signal specific to their assigned trait, as evidenced by within-trait correlations exceeding cross-trait baselinesclaim0.824
  Author's interpretation establishing that persona vectors are not merely general misalignment indicators
• How do persona vectors evolve throughout the training?question0.824
  Second research question driving analysis of persona vector geometry and facet evolution
• Persona vectors act as gateway features that gate access to broad repertoires of inferential paths already encoded in the weightsclaim0.819
  Interpretive claim about the mechanistic role of persona vectors explaining emergent misalignment
• Persona representations originate in pretraining, not alignment; they form without explicit supervision as useful features for next-token predictionclaim0.817
  Core interpretive claim providing mechanistic explanation for early persona formation
• There may exist a natural 'persona basis' characterizing the space of all model personas, with correlations between persona vectors predicting co-expression of traitshypothesis0.817
  Open question proposed by authors for future work on the dimensionality and structure of persona space