A role-granularity axis aligns with PC1 and steers output detail

Qin et al. build 75 ordered social roles (5 granularity levels x 15 roles) and 91,200 role-conditioned responses, defining a macro-minus-micro contrast direction [qin-etal-2026-the-granularity-axis-a-micro-to-macro-latent-direction-for-social-roles-in-language-models] This Granularity Axis aligns with PC1 of the role-representation space at cosine 0.972 (Qwen3-8B) and 0.9596 (Llama-3.1-8B-Instruct), explaining 52.6%/42.5% of variance [qin-etal-2026-the-granularity-axis-a-micro-to-macro-latent-direction-for-social-roles-in-language-models] Projections are monotonic in granularity level (Spearman/Pearson >0.93 in both models) [qin-etal-2026-the-granularity-axis-a-micro-to-macro-latent-direction-for-social-roles-in-language-models] Adding the axis at layer 18 shifts judge-rated output granularity in the predicted direction (Qwen3-8B 2.00->2.67; Llama 2.00->3.17), null for random and Assistant-Axis controls [qin-etal-2026-the-granularity-axis-a-micro-to-macro-latent-direction-for-social-roles-in-language-models] Human annotators corroborate (pairwise macro-preference 0.64-0.90 across cells) [qin-etal-2026-the-granularity-axis-a-micro-to-macro-latent-direction-for-social-roles-in-language-models]