arXiv AI June 2, 2026 By Rashid Mushkani

Benchmarks for Vision-Language Models in Urban Perception Should Be Reliability-Aware and Negotiated

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

3d ago

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

arXiv:2601.08355v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for sa...

By Guo Cheng, Huang Li

llms agents computer-vision robotics multimodal benchmarks safety

arXiv Machine Learning

Aug 21

From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City

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.

By Joan Perez, Giovanni Fusco

llms multimodal

arXiv AI

Jun 17

m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning

arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.

By Yosub Shin, Michael Buriek, Igor Molybog

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Sep 1

Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors

arXiv:2503.00610v2 Announce Type: replace-cross Abstract: Understanding how people perceive urban environments is essential for inclusive planning, yet conventional surveys are costly and difficult t...

By Ciro Beneduce, Bruno Lepri, Massimiliano Luca

llms multimodal safety

arXiv AI

Jul 17

AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery

arXiv:2607. 14756v1 Announce Type: new Abstract: This research investigates the potential of Vision-Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images.

By Zahratu Shabrina, Muhammad Asa, Jin Rui, Lu Yin, Stephen Law

llms multimodal benchmarks

arXiv AI

Jul 10

AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding

arXiv:2607. 08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering.

By Siddharth Damodharan, Radhika Gupta, Ali Alshami, Ryan Rabinowitz, Jugal Kalita

llms agents nlp robotics multimodal benchmarks safety

One email a morning, machine-written

Related stories

Semantic Misalignment in Vision-Language Models under Perceptual Degradation

From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City

m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning

Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors

AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery

AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding