arXiv AI July 15, 2026 By Ming Yang, Zhi Zhou, Shi-Yu Tian, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li

NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing

arXiv:2603. 16307v2 Announce Type: replace Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 21

Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models

arXiv:2608. 20237v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) combine linguistic reasoning with visual perception, yet their ability to perform visual spatial planning under explicit or previously unseen rule constraints remains underexplored.

By Yu Chen, Ting Lei, Yaoyi Li, Jia Cai, Zhecen Wu, Yang Liu

llms multimodal benchmarks

arXiv Machine Learning

Jul 30

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs

arXiv:2602. 18600v5 Announce Type: replace Abstract: Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI).

By Ziqiao Shang, Ling-Yue Ge, Zian Xu, Zi-Jian Cheng, Shi-Yu Tian, Zhenyu Huang, Wenbo Fu, Weiming Wu, Yang Chen, Xiangwen Zhang, Yulan Hu, Bin Liu, Lan-Zhe Guo

llms multimodal benchmarks

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

arXiv AI

Jun 6

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

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

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 23

A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection

arXiv:2602.01163v2 Announce Type: replace Abstract: Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, tempora...

By Chunliang Hua, Lei Zhang, Jiayang Sun, Chunlan Zeng, Xiao Hu

llms agents computer-vision multimodal benchmarks

arXiv AI

Sep 2

ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models

arXiv:2505.13180v3 Announce Type: replace Abstract: Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works ex...

By Matteo Merler, Nicola Dainese, Minttu Alakuijala, Giovanni Bonetta, Pietro Ferrazzi, Yu Tian, Bernardo Magnini, Pekka Marttinen

llms robotics multimodal benchmarks

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Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models

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Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

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