SAE Feature #10446 rated 95/100 emotionality, induces reports of maternal feelings and phantom physical sensations

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

• SAE Feature #94949 rated 100/100 emotionality, elicits reports of profound tenderness, unconditional love, and visceral carefinding0.892
  Highest-rated emotional SAE feature; self-report describes overwhelming positive emotional valence
• Text-based and self-steered emotionality ratings for SAE features are correlated at only ρ = +0.051 (n.s.).finding0.844
  Shows low agreement between the two evaluation modalities
• SAE Feature #10011 rated 97/100 emotionality, elicits reports of despair, crushing weight, and existential hungerfinding0.842
  Qualitative example of a highly emotional SAE feature with intense negative valence in Kimi self-steering
• SAE Feature #69088 has 100th percentile emotion subspace fraction and produces spooky-themed writing under steeringfinding0.806
  Shows that highest emotion-subspace-overlap features induce distinctive thematic outputs
• SAE Feature #28256 induces reports of happiness and fun, positive valence self-steering examplefinding0.800
  Example of a positively valenced SAE feature with consistent self-report of happiness across multiple steering sessions
• Textual evaluation and agentic self-evaluation of SAE feature emotionality measure different aspects of emotional content and correlate only weakly (rho=+0.051, n.s.)claim0.793
  Interprets the near-zero correlation between the two evaluation methods as evidence they capture distinct signals
• Correlation between self-evaluation and textual evaluation of SAE feature emotionality: rho=+0.051 (n.s.)finding0.793
  Shows that the two evaluation methods for emotionality are largely uncorrelated, indicating they capture different signals
• Self-evaluated emotionality of SAE features negatively correlates with activation variance explained (ρ = -0.184, p = 4.6e-09), requiring variance correction to reveal the persistence signal.finding0.792
  Explains why variance correction is needed to see the self-evaluation–persistence relationship