The paper examines how multimodal language models, specifically Qwen3‑VL‑8B‑Instruct, encode different observer perspectives in their internal representations. By varying persona content and prompt framing across 100 images and 53 conditions, the study finds that early layers encode image identity while later layers develop persona‑specific structures. Prompt framing more strongly alters internal representations than persona identity, yet persona content has a larger effect on behavioural judgments, revealing a dissociation between representational geometry and functional outcomes.
arXiv:2604.17031v3 Announce Type: replace Abstract: The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach th...
arXiv:2606. 03093v1 Announce Type: new Abstract: Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes reshape internal representations to produce behavior.
arXiv:2602. 15892v2 Announce Type: replace-cross Abstract: Visual perspective taking--inferring how the world appears from another's viewpoint--is foundational to social cognition.
arXiv:2508.09852v2 Announce Type: replace-cross Abstract: How can models help people communicate unusual perceptual experiences without changing what they mean? A recognizable image is only part of t...
arXiv:2606. 18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
The paper investigates how persona prompting influences the language produced by two multimodal large language models—Qwen3‑VL and Gemma4—when describing urban scenes. Outputs are categorized into descriptive grounding (captions), perception tags, and interpretive framing (justifications). Results show captions largely converge across persona profiles, while justifications differ markedly, especially along economic status, political orientation, and personality dimensions, with economic status producing the greatest variation. Perception tags also reflect attribute similarities, and exploratory topic analysis indicates persona‑specific evaluative emphasis. Overall, persona prompting has a stronger effect on interpretive framing than on descriptive grounding.
The paper compares two approaches to pragmatic intent recognition in human‑robot interaction: FISER, which uses structured symbolic reasoning, and CEMIR, which enriches textual prompts with synthetic narratives and images. Experiments across four large language models on the HandMeThat v2 benchmark show no statistically significant performance difference between the two methods, regardless of model size or ambiguity difficulty. The results indicate that when task constraints are already present in the text, multimodal enrichment offers no measurable advantage over structured reasoning.
arXiv:2605. 09159v2 Announce Type: replace Abstract: Recent work shows that large language models (LLMs) encode behavioral traits ("personas") as linear directions in activation space, often called "persona vectors".
The paper investigates how visual and textual information are fused in Multimodal Large Language Models (MLLMs). By analyzing concatenation and native multimodal architectures through alignment decoupling, attention routing, entropy, intrinsic dimensionality, and causal interventions, the authors uncover two distinct fusion pathways: concatenation models use a text‑first, vision‑later strategy, while native models integrate vision and text earlier and reorganize feature spaces. The study also employs visual CKA to test the Platonic Representation Hypothesis, offering a mechanistic view of multimodal fusion and informing architecture‑aware diagnostics.
The paper investigates whether vision‑language models (VLMs) can communicate visually in ways similar to humans. It reports that simple visual symbols, such as a red heart, can unexpectedly steer VLMs toward recursive solutions in unrelated tasks, revealing a generation‑interpretation gap where models produce intuitive shapes that do not effectively influence other models. In more complex communication games, VLMs can creatively use visuals to convey abstract concepts, though some messages remain unintuitive to humans, highlighting both the potential and the challenges of visual communication between humans and VLMs.
arXiv:2605.12850v3 Announce Type: replace-cross Abstract: Fine-tuning large language models on narrow data with harmful content produces broadly misaligned behavior on unrelated prompts, a phenomenon...