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MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?
Shiqi Dai, Zizhi Ma, Zhicong Luo +8
While Multimodal Large Language Models (MLLMs) have exhibited remarkable general intelligence across diverse domains, their potential in low-altitude applications dominated by Unma…
Scaling Spatial Reasoning in MLLMs through Programmatic Data Synthesis
Zhi Helu, Huang Jingjing, Xu Wang +9
Embodied intelligence, a grand challenge in artificial intelligence, is fundamentally constrained by the limited spatial understanding and reasoning capabilities of current models.…
SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios
Juntao Cheng, Wanyue Zhang, Zhiwei Yu +7
Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments. Interacting w…
Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning
Yibin Huang, Wang Xu, Wanyue Zhang +6
Spatial intelligence is a critical frontier for Multimodal Large Language Models (MLLMs), empowering them to comprehend the physical world. Drawing inspiration from human perceptio…
Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Wanyue Zhang, Yibin Huang, Yangbin Xu +5
Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, ex…