arXiv:2606. 06217v1 Announce Type: cross Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints.
By Tan Zhang, Quanyou Li, Lu Zhang, Jun Liu, Xiaofeng Zhu, Ping Hu
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
LADBench is a new benchmark comprising over 1,000 synthetic images that contain logical anomalies across four domains—Residential, Urban, Collaborative, and Nature. It introduces a Tiered Prompting Protocol that progressively discloses information to test how much explicit help a model needs to locate and reason about these faults. Evaluation of leading foundation models shows that even the best only reaches 70.11% accuracy, indicating that implicit logical fault detection remains largely unsolved.
By Sahasra Kondapalli, Lara Radovanovic, Aadi Palnitkar, Mingyang Mao, Xiaomin Lin
arXiv:2604. 09686v2 Announce Type: replace Abstract: Traditional neural network models for intent inference rely heavily on observable states and struggle to generalize across diverse tasks and dynamic environments.
By Anshul Nayak, Shahil Shaik, Yue Wang
arXiv:2606. 02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB images, point clouds, depth maps and camera poses.
By Hilton Raj, Vishnuram AV
arXiv:2606. 16783v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual reasoning but rely on text-based chain-of-thought (CoT), lacking interpretable visual intermediates.
By Zhiqiang Zhou, Junliang Dai, Xu ling