# The Geometry of Simulated Perspectives: Dissociating Representation and Behaviour in a Multimodal Language Model

**Authors:** Mathis Immertreu, Fitim Abdullahu, Thomas Kinfe, Claus Metzner, Helmut Grabner, Patrick Krauss, Achim Schilling  
**Lab:** ZHAW Zurich University of Applied Sciences · **Published:** 5 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.08262) · [PDF](https://arxiv.org/pdf/2611.08262) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.08262.html)

## Key numbers

- **100**: Across 100 images and 53 prompting conditions

## Abstract

Multimodal language models can simulate different observers, yet it remains unclear how assigned perspectives reshape their internal representations and whether these changes govern their judgements. Here, we investigate the representational geometry of persona-conditioned visual interestingness in Qwen3-VL-8B-Instruct. Across 100 images and 53 prompting conditions, we systematically vary persona content and the linguistic framing used to introduce it, while recording behavioural ratings and layer-wise hidden states. We observe a systematic reorganisation of representational geometry across processing depth: early layers primarily encode image identity, whereas persona-specific structures emerge in later layers. Strikingly, prompt framing reshapes internal representations more strongly than persona identity, whereas persona content has a greater impact on behavioural judgements. This dissociation is further illustrated by minimal identity statements that alter representations even without changing responses. These findings demonstrate that representational distance does not necessarily reflect behavioural relevance. More broadly, they reveal how simulated perspectives emerge as distinct structures within the representational space of artificial cognitive systems, highlighting a fundamental distinction between the geometry of internal representations and their functional consequences.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.