BD-HazardVLM is a diagnostic benchmark of 500 Bangladesh road-scene images designed to probe vision‑language models’ defensive‑driving reasoning in weakly structured Global South traffic. The benchmark includes annotations for hazard presence, category, risk timing, severity, defensive actions, visible evidence, and bias traps, and evaluates models such as Qwen2.5‑VL‑7B, InternVL3‑8B, and MiniCPM‑V‑4.6. Results show high binary hazard‑presence accuracy but poor fine‑grained reasoning, with category accuracy below 40 %, risk‑timing accuracy under 13 %, and defensive‑action matching under 2 %.
whyItMatters":"The study demonstrates that coarse hazard recognition does not guarantee reliable fine‑grained defensive‑driving reasoning, highlighting the need for geographically diverse safety evaluation of vision‑language models."
By Abdullah Al Maruf, Md. Sajedul Islam, Tanmoy Mridha, Irfan Hossain Bhuiyan
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.
By Tianshi Wang, Jingsong Wang, Fengling Li, Jingjing Li, Xin Li, Lei Zhu
arXiv:2607. 18325v1 Announce Type: cross Abstract: Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies.
By Murali Indukuri, Mohammad Eskandari, Sree Nitya Kollu, Stephanie Lukin, Cynthia Matuszek
arXiv:2608.23313v1 Announce Type: new
Abstract: Vision-language model safety benchmarks typically evaluate only final responses: whether a model refuses, warns, or complies. This outcome-level view c...
By Xuetong Li, Gaofeng Liu
arXiv:2608. 08077v1 Announce Type: new Abstract: Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability.
By Gabriele La Malfa, Nitay Alon, Emanuele La Malfa, Reuth Mirsky, Stefan Sarkadi
arXiv:2610.07514v1 Announce Type: new
Abstract: A vision-language model may need to combine an image with a prompt to recognize a safety risk that neither reveals alone. Where does this joint safety...
By Faezeh Dehghan Tarzjani, Mevan Wijewardena, Alexander Romanus, Sampad Mohanty
The paper investigates why vision‑language models (VLMs) often miss hazards, attributing this to an overly conservative decision criterion rather than poor perception. By identifying and steering an affective ‘fear’ circuit within the model, the authors shift the decision threshold, dramatically increasing hazard recall from 27.0 % to 75.7 % without retraining. The study demonstrates that affect‑based inference-time manipulation can correct VLM decision biases in practical applications.
By Xiaoshan Zhou
arXiv:2608. 10954v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions.
By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao
arXiv:2603. 03989v2 Announce Type: replace-cross Abstract: When visual evidence is ambiguous, vision models must decide how to interpret face-like patterns.
By Qianpu Chen, Derya Soydaner, Rob Saunders
arXiv:2607. 00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories.
By Siddhant Panpatil, Arth Singh, Mijin Koo, Chaeyun Kim, Haon Park, Dasol Choi
arXiv:2506.09557v2 Announce Type: replace-cross
Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorat...
By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao
As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes a safety imperative. Existing mechanis...