arXiv AI

Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?

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.

arXiv Computer Vision

4d ago

SafeVantage: Vantage-Aware Memory for Reliable Embodied Decisions

arXiv:2609.36906v1 Announce Type: new Abstract: Reliable embodied decisions under partial observability require informative observations and sufficient supporting evidence. However, semantic scores a...

By Sean Hardesty Lewis, Zuyi Guo, Benwang Chen, Zirui Liu, Hongyi Lin, Heye Huang

robotics multimodal benchmarks

arXiv AI

Sep 17

Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation

Mem2Ego introduces a vision‑language model for embodied navigation that combines global memory with egocentric visual inputs. By adaptively retrieving task‑relevant cues from a global memory module and aligning them with local perception, the framework improves spatial reasoning and decision‑making over long horizons. The method outperforms prior state‑of‑the‑art approaches on the HSSD and HM3D benchmarks and shows strong performance on a real robot.

By Lingfeng Zhang, Yuecheng Liu, Zhanguang Zhang, Matin Aghaei, Yixin Xiao, Yaochen Hu, Mohammad Ali Alomrani, David Gamaliel Arcos Bravo, Hongjian Gu, Zhiyuan Li, Yangzheng Wu, Zhanpeng Zhang, Raika Karimi, Atia Hamidizadeh, Guowei Huang, Haoping Xu, Tongtong Cao, Weichao Qiu, Xingyue Quan, Jianye Hao, Yuzheng Zhuang, Yingxue Zhang

llms agents robotics multimodal benchmarks

arXiv AI

Jun 9

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.

By Hongcheng Gao, Hailong Qu, Jingyi Tang, Jiahao Wang, Zihao Huang, Hengkang Qiao, Shihong Huang, Junming Yang, Yi Li, Hongyixuan Yuan, Wenjie Li, Bohan Zeng, Wenbo Li, Bo Wang, Jianhui Liu, Olive Huang, Haoyang Huang, Wentao Zhang, Guoqing Huang, Nan Duan, Yinpeng Dong

llms agents multimodal benchmarks

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

Jun 12

Foresight: Iterative Reasoning About Clues that Matter for Navigation

arXiv:2606. 12550v1 Announce Type: cross Abstract: Open-world mapless navigation from sparse language instructions requires resolving underspecified goals and inferring which environmental cues are relevant for reaching the goal.

By Arthur Zhang, Carl Qi, Donne Su, Xiangyun Meng, Amy Zhang, Joydeep Biswas

llms reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jun 2

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

arXiv:2602. 08236v2 Announce Type: replace-cross Abstract: Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints.

By Shoubin Yu, Yue Zhang, Zun Wang, Jaehong Yoon, Huaxiu Yao, Mingyu Ding, Mohit Bansal

llms robotics benchmarks

arXiv AI

Aug 10

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

arXiv:2603. 06054v2 Announce Type: replace-cross Abstract: The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios.

By Nikos Theodoridis, Reenu Mohandas, Ganesh Sistu, Anthony Scanlan, Ciar\'an Eising, Tim Brophy

llms agents multimodal benchmarks

arXiv AI

Jul 17

SafeRelBench: A Spatial-Relation-Aware Benchmark for Process-Level Safety in VLM-Driven Embodied Agents

arXiv:2607. 14543v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used as the reasoning backbone of embodied agents, enabling robots to interpret visual scenes, follow language instructions, and plan multi-step actions.

By Huaigang Yang, Ya Li, Min Ren, Bo Dai, Zhenliang Zhang, Zhaofeng He

llms agents robotics multimodal benchmarks safety

arXiv AI

Aug 11

MetaSpace: Metamorphic Testing for Spatial Cognition in Embodied Agents

arXiv:2608. 07533v1 Announce Type: new Abstract: An embodied agent is an intelligent entity that interacts with its environment through a physical body.

By Gengyang Xu, Dongwei Xiao, Yiteng Peng, Shuai Wang

agents nlp robotics multimodal benchmarks safety

arXiv AI

Jun 17

Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models

arXiv:2606. 17389v1 Announce Type: cross Abstract: Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical.

By Logan Mann, Yi Xia, Ajit Saravanan, Ishan Dave, Saadullah Ismail, Shikhar Shiromani, Emily Huang, Ruizhe Li, Kevin Zhu

llms agents multimodal

arXiv Machine Learning

Jul 23

Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning

arXiv:2605. 31119v2 Announce Type: replace-cross Abstract: In robotics, dangers and adversity modes are often embodiment-specific and relative to each agent.

By Navin Sriram Ravie, Andrew Jong, Krrish Jain, John Liu, Omar Alama, Bijo Sebastian, Sebastian Scherer

llms agents robotics multimodal

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