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

Lingpeng Kong, Jing Xiong, Xiachong Feng, Xiaocheng Feng, Ziyun Song, Chonghan Qin

arXiv · PDF · Open in the Atlas

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Abstract

Large language models (LLMs) are routinely prompted to take on social roles ranging from individuals to institutions, yet it remains unclear whether their internal representations encode the granularity of such roles, from micro-level perspectives centered on individual experience to macro-level perspectives associated with organizational, institutional, or national reasoning. We find that they do: a contrast-based Granularity Axis, defined as the difference between mean macroand micro-role hidden states, aligns with the principal axis (PC1) of the role representation space at cosine 0.972 and accounts for 52.6% of its variance in Qwen3-8B. Granularity is therefore not one factor among many but the dominant geometric axis along which prompted social roles are organized. To establish this result, we construct an ordered set of 75 social roles spanning five granularity levels and collect 91,200 role-conditioned responses across shared question sets and prompt variants, from which we extract role-level hidden states and project them onto the axis. Role projections increase monotonically across all five levels, and the structure remains stable across layers, prompt variants, and score-filtered subsets, and transfers to Llama-3.1-8B-Instruct. The axis is not merely descriptive but causal: intervening along it shifts response granularity in the predicted direction, with Llama moving from 2.00 to 3.17 on a five-point macro scale under positive steering on prompts that admit genuinely local responses. The two models differ in how this control behaves, indicating that controllability along the axis depends on each model's default operating regime rather than on whether the direction exists. Together, these findings reposition social role granularity from a stylistic surface phenomenon to a representational primitive: a single, ordered, causally manipulable direction that organizes role-conditioned generation across model families and exposes social scale as a controllable axis of LLM behavior. 2 * Corresponding author. 2 Code and data are available at Granularity-Axis.

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