Reported emergence times are conservative lower bounds since persona representations may exist before they become linguistically accessible to extraction

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Persona representations originate in pretraining, not alignment; they form without explicit supervision as useful features for next-token predictionclaim0.772
  Core interpretive claim providing mechanistic explanation for early persona formation
• Representational dynamics of causal emergence align with reward improvement in most tasks.finding0.761
  The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.
• We suspect it has to do with personas being latent factors that persist for many tokens; thus, recent expression of a persona should predict its near-future expression.quote0.755
  Load-bearing mechanistic conjecture about why persona vectors generalize from extraction to prediction
• Causal emergence identification tasks can be understood as causal representation learning tasks.claim0.755
  Authors propose a conceptual mapping between CE identification and CRL.
• 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 dataclaim0.752
  Interpretive finding against a unified emergence threshold for all personas
• Personas are latent factors that persist for many tokens, so recent expression of a persona predicts near-future expressionclaim0.749
  Author's hypothesis explaining why persona vectors extracted from exhibited-trait activations generalize to causal influence
• Object models in which time, versioning, causality, etc., are significant are probably far better modelled by considering the time component as another key rather than an intrinsic property of the underlying model.claim0.745
  Claim that orthogonal dimensions like time should be explicit keys in the associative model.
• Random word prefix prompts show emergence patterns similar to no-prompt, suggesting prompt length alone does not shift truth geometry.claim0.744
  Control experiment ruling out token-count as the cause of truth geometry shifts.