The paper introduces PanicCognitivePath (PCP), a model that predicts the timing of panic emotional arousal by integrating appraisal emotion theory into a Belief‑Desire‑Emotion‑Intention (BDEI) framework. PCP uses a Psychological Safety Distance (PSD) model to fuse physical, social, cognitive, and informational signals into a unified risk metric, and confines large language models to a single parameter‑estimation step to reduce hallucinations. Experiments on Hurricane Sandy data show PCP improves individual prediction accuracy by 10.68% and reduces peak count error to 7.07%.
By Mengzhu Liu, Long Qin, Chuan Ai, Zhengqiu Zhu, Hongru Liang, Fangfang Li, Chen Gao, Yong Li, Xin Lu, Quanjun Yin
Vision-language models (VLMs) increasingly support construction hazard monitoring, yet their frequent false alarms remain poorly understood. This study investigates whether false struck-by hazard warn...
The paper introduces activation probes—directions in a language model’s residual stream—that can both detect and steer safety‑relevant behaviors such as deception, high‑stakes awareness, and sycophancy. By evaluating different estimators (difference‑of‑means vs. logistic regression) and token‑pooling schemes (last, mean, learned attention) on Llama‑3.1‑8B‑Instruct, the authors find that logistic regression excels at detection while difference‑of‑means excels at steering, except for sycophancy where the pattern reverses. The study highlights that the optimal pooling method, layer choice, and steering metric vary by behavior and data, and provides exported probes that enable an interactive interface for scoring and steering text.
By Nimun Bajwa, Massimo Poesio
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. 02089v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning.
By Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy, Hung Viet Nguyen, Huy Nguyen Minh Nhat, Thanh-Huy Nguyen, Cuong Tuan Nguyen, Hoang M. Le, Dat Nguyen, Phat Kim Huynh, Min Xu, Ulas Bagci
arXiv:2609.36563v1 Announce Type: new
Abstract: Visual emotion recognition commonly assumes that all evidence required for prediction is contained in the observed image or video. Yet the same visible...
By Yihao Qian, Runhao Zeng, Sicheng Zhao, Feng Liang, Hongmin Cai, Mingkui Tan
arXiv:2606. 30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated.
By Max Fomin, Elad David, Amit LeVi
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: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:2608.30750v1 Announce Type: new
Abstract: Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content in...
By Jiaxuan Li, Jiahao Zhang, Duc Minh Vo, Huy H. Nguyen, Pride Kavumba, Koki Wataoka
The paper proposes an agentic framework for multimodal emotion reasoning that uses frozen models to avoid costly instruction tuning. It employs modality‑specific perception agents, an integration agent, and an MLLM critic to generate emotion predictions with evidence citations, followed by an evidence‑grounded verification stage that identifies errors and applies targeted prompt patches without updating model weights. On the MER2025 dataset, this approach improves F‑score across several large language‑vision models, outperforming static pipelines and matching or exceeding reflection baselines.
By Elyas Rashno, Farhana Zulkernine
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.
By Edward Sun, Yuchen Wu, Zixian Ma, Eric Hanchen Jiang, Yijia Xiao, Xiaoyuan Yi, Ranjay Krishna, Wei Wang, Jindong Wang, Aylin Caliskan