arXiv:2611.05954v1 Announce Type: cross Abstract: Pedestrians increasingly encounter robots in urban public spaces without interacting with them, but how robots change the perceived safety of a stree...
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.
Large language models (LLMs) are increasingly used to guide urban safety decisions, but this study shows that their judgments are more influenced by neighborhood names than by geographic coordinates. Across seven instruct‑tuned models tested on 186 neighborhoods in Los Angeles and Chicago, name‑based ratings varied significantly and correlated with the proportion of locally dominant marginalized groups, while coordinate‑only ratings remained largely flat. The research finds that removing neighborhood names reduces both bias and accuracy, highlighting the complex role of demographic stereotypes and crime signals in LLM safety assessments.
arXiv:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
The paper introduces KoreaGEO, a fine‑grained, multimodal, privacy‑aware benchmark for image‑based geolocation built from Korean street views. It covers four socio‑spatial clusters, nine place types, and includes two captioning styles that mimic real‑world privacy exposure. Using a three‑path protocol (image‑only, functional‑caption, high‑risk‑caption), the study shows that input modality has a stronger effect on localization accuracy and privacy risk than model size or architecture, and reveals structural prediction biases toward core cities.
The paper introduces a vision‑language framework that extracts social indicators from street‑level imagery, converting panoramic views into sidewalk‑facing sideviews with timestamps. Using a VLM‑based activity detection system, it codes each pedestrian across ten observable dimensions, producing a Social Dwelling Index (SDI) that captures grouping, dwelling, activity diversity, and accessibility flags. Applied to over 100,000 sideviews in New York City, the study finds that pedestrian volume and SDI are only weakly correlated, indicating that high foot traffic does not necessarily equate to intense social activity.
arXiv:2606. 00871v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used to generate structured descriptions of street-level imagery for tasks such as streetscape auditing, mapping, and public consultation.
The paper introduces Counterfactual Neighborhood Risk Fields (CNRF), an evidence‑grounded framework that improves safety for vision‑language models (VLMs). CNRF builds context‑matched benign‑harmful counterfactual pairs, uses their representation differences to define risk directions, and separates neighborhood support from risk estimation. This approach allows selective intervention rather than blanket rejection, achieving high macro defense success rates across multiple VLMs while preserving benign‑task utility.
The paper introduces an explainable pedestrian routing method that estimates perceived safety by first generating a natural‑language caption from street‑level images and then deriving risk scores solely from structured features of that caption. Benchmarking nine captioning setups against a CLIP image‑embedding baseline shows comparable performance, and the system was deployed on over 650,000 images across 36 wards in Manchester and Huddersfield. Field validation with 3,669 ratings from 70 participants revealed a modest but statistically significant correlation (r = 0.262) with human judgments, while a stronger supervised benchmark did not translate into better real‑world performance.
The paper investigates how large language and visual‑language models used in autonomous vehicles inherit human driver biases when deciding whether to yield to pedestrians. It introduces two new bias‑testing methods—All Else Being Equal and Self‑Consistency tests—to evaluate these models. Results reveal that the models’ yielding decisions are influenced by pedestrian attributes such as gender, ethnicity, religion, disability, age, skin tone, and socio‑economic status, with varying patterns across models.
arXiv:2608. 20026v1 Announce Type: cross Abstract: Streetscape quality has become a central concern in contemporary urban planning, particularly within the framework of the pedestrian-friendly 15-minute city, where walkability and public-space quality are increasingly recognized as key determinants of urban performance.
The paper introduces MPS-Bench, a benchmark of 5,181 scenarios from 584 real-world images across 12 high-risk domains, each paired with a hidden user profile, to evaluate personalized safety in vision‑language models (VLMs). Eight leading VLMs were tested and found to almost always respond directly (86‑99%) without seeking missing context, scoring no higher than 2.6/5 on personalized safety. The authors identify a phenomenon called visual dominance, where visual information enters text representations early and suppresses textual risk signals, and propose PRISM, a lightweight input monitor that predicts when a query should be deferred, achieving 0.978 AUC and outperforming all tested models on the safety‑utility Pareto frontier.