arXiv Computer Vision

Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding

The paper introduces a new benchmark for evaluating Multi‑Modal Large Language Models (MLLMs) on visual negation understanding, focusing on safety-critical scenarios. It defines the Scene Negation Understanding under Safety Cognition (SNUS) task and presents a high‑fidelity negative caption dataset that maps dense assertions of localized hazards. The authors also propose the Cognitive Expected Scene Graph (CESG) Score, a polarity‑aware, structure‑grounded metric that remains robust under semantic reversals, revealing that existing models and traditional metrics fail on this task.

arXiv Computer Vision

Sep 18

Absence is Presence: Understanding Visual Scene Negative Events Under Safety Cognitive Constraint

The paper introduces a new task called visual scene negative captioning, which aims to describe what should be present in an image but is actually absent, a capability crucial for safety-critical applications. It proposes the CRCD framework, which uses counterfactual reconstruction and contrastive decoding to overcome affirmation bias, limited mental filling, and representation bias. CRCD employs a dual-branch architecture for amodal completion and functional association, along with multi-condition representation learning, to generate accurate negative captions and sets a high-performance baseline for this emerging task.

By Zhiyun Jiang, Hanyong Wang, Binbin Liang, Yu Xie, Menglong Yang, Wei Li

llms multimodal safety

arXiv AI

4d ago

ThinkingGuard: Decoding Implicit Hazards via Step-by-Step Risk Attribution in Multimodal Large Language Models

arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal im...

By Ruochen Zhang, Yao Huang, Yitong Sun, Jiahe Xie, Jin Yan, Jifan Ma, Yuanfang Guo, Xingxing Wei

llms multimodal benchmarks safety

arXiv AI

Jun 24

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.

By Xiaowei Gao, Pengxiang Li, Yitai Cheng, Ruihan Xu, James Haworth, Stephen Law, Yun Ye

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 19

Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

arXiv:2606. 20177v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks.

By Haochen Han, Jue Wang, Alex Jinpeng Wang, Fangming Liu

llms multimodal benchmarks safety

Hugging Face Trending Papers

Jun 23

UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving

Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide limited grounded evidence for their explanations.

llms agents robotics multimodal benchmarks safety

arXiv AI

Sep 1

WaymoQA: A Multi-View Visual Question Answering Dataset for Safety-Critical Reasoning in Autonomous Driving

arXiv:2511.20022v3 Announce Type: replace-cross Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...

By Seungjun Yu, Seonho Lee, Namho Kim, Jaeyo Shin, Junsung Park, Wonjeong Ryu, Raehyuk Jung, Hyunjung Shim

llms agents nlp robotics fine-tuning multimodal safety

arXiv Computation and Language

Aug 31

Steering Multimodal Large Language Models Decoding for Context-Aware Safety

The paper introduces Safety-aware Contrastive Decoding (SafeCoDe), a lightweight, model‑agnostic framework designed to improve context‑aware safety in Multimodal Large Language Models (MLLMs). SafeCoDe operates in two stages: a contrastive decoding step that highlights tokens sensitive to visual context by contrasting real and Gaussian‑noised images, and a global‑aware token modulation strategy that adjusts refusals based on scene‑level reasoning and predicted safety verdicts. Experiments across various MLLM architectures and safety benchmarks demonstrate that SafeCoDe consistently enhances context‑sensitive refusal behaviors while maintaining model helpfulness.

By Zheyuan Liu, Zhangchen Xu, Guangyao Dou, Xiangchi Yuan, Zhaoxuan Tan, Radha Poovendran, Meng Jiang

llms multimodal benchmarks safety

arXiv AI

Aug 12

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

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

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Sep 1

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

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

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 2

Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks

arXiv:2603. 11689v3 Announce Type: replace Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks.

By Mei Chee Leong, Ying Gu, Hui Li Tan, Liyuan Li, Nancy Chen

llms multimodal benchmarks

arXiv AI

Jun 2

Mitigating Perceptual Judgment Bias in Multimodal LLM-as-a-Judge via Perceptual Perturbation and Reward Modeling

arXiv:2606. 02578v1 Announce Type: cross Abstract: Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a critical weakness: when visual evidence conflicts with textual cues, MLLM judges tend to reward plausible narratives over perceptually correct answers.

By Seojeong Park, Jiho Choi, Junyong Kang, Seonho Lee, Jaeyo Shin, Hyunjung Shim

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Aug 25

GuardianBench: A Same-Scene Instruction-Contrastive Benchmark for Latent Contextual Risk in Embodied AI

arXiv:2608.21928v1 Announce Type: new Abstract: In embodied AI, safety risk can be latent: a benign instruction and a safe scene become hazardous only when composed. Prior work has advanced embodied...

By Zhesheng Zhang, Jiahao Lu, Wei Liu, Cong Pan, Jianhua Yang, Yixiang Chen, Hongyuan Yu, Mengqi Zhang, Kailin Lyu, Zhumin Chen, Keji He

llms robotics multimodal benchmarks safety

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