The Granularity Axis: A Micro-to-Macro Latent Direction for Social Roles in Language Models

Large language models (LLMs) are prompted to adopt social roles from individuals to institutions, but whether their representations encode this difference in social granularity remains unclear. We discover a contrast-based Granularity Axis, the difference between mean macro- and micro-role hidden states, that aligns with the dominant geometry of role space: in Qwen3-8B, its cosine with PC1 is $0.972$, and PC1 explains $52.6\%$ of the variance. Across 75 roles at five levels, with 90,000 role-conditioned and 1,200 default responses per model, projections increase monotonically and remain stable across layers, prompts, and response-quality filters. The structure transfers to Llama-3.1-8B-Instruct and scales to Qwen3-32B and Llama-3.1-70B-Instruct, whose PC1 shares are $45.99\%$ and $31.23\%$. Independent five-rater validation ($\alpha =0.961$) preserves the ordering; identical continuations and affect/formality controls confirm scale-related variation beyond generated wording. The axis is also behaviorally causal: positive steering moves Llama from $2.00$ to $3.17$ on a five-point macro scale for prompts admitting local responses. Steering strength and stability depend on the model's default operating regime. Together, these findings establish social granularity as a representational primitive: a continuous, ordered, causally manipulable direction organizing role-conditioned generation across model families. Code and data are available at https://anonymous.4open.science/r/Granularity-Axis-anon/.