Vision–language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due to the large number of vision tokens processed during the prefill stage. Existing token pruning methods often rely on utilizing the static attention patterns directly, failing to exploit the dynamic internal signals within VLMs. To address the issue, we propose AdaptInfer, a novel plug-and-play framework for vision token pruning. First, we introduce a dynamic text-guided pruning mechanism that construct soft priors over text-token importance on each pruning layer, allowing more informed scoring of vision tokens at each stage. Second, we observe a highly consistent distribution of cross-modal attention shifts by architecture, which inspires us to introduce a efficient data-driven schedule which determines the pruning locations automatically. Experimental results have verified the effectiveness and generalization of the proposed method. Under the same token budget, AdaptInfer surpasses existing approaches in accuracy. For instance, AdaptInfer maintains averagely 99.4% accuracy on Qwen2-VL while 70% of the prefilling vision token overhead is reduced. The source code is available on: https://github.com/weiczh02/AdaptInfer-base.

Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. We introduce a benchmark to evaluate whether video-large language models (Video-LLMs) can naturally process continuous first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. We have manually control drones to collect 3D embodied motion video data from real-world cities and simulated environments, resulting in 1.5k video clips. Then we design a pipeline to generate 5.2k multiple-choice questions. Evaluations of 17 widely-used Video-LLMs reveal current limitations in urban embodied cognition. Correlation analysis provides insight into the relationships between different tasks, showing that causal reasoning has a strong correlation with recall, perception, and navigation, while the abilities for counterfactual and associative reasoning exhibit lower correlation with other tasks. We also validate the potential for Sim-to-Real transfer in urban embodiment through fine-tuning.

Aerial vision-and-language navigation (VLN) — requiring drones to interpret natural language instructions and navigate complex urban environments — emerges as a critical embodied AI challenge that bridges human-robot interaction, 3D spatial reasoning, and real-world deployment. Although existing ground VLN agents achieved notable results in indoor and outdoor settings, they struggle in aerial VLN due to the absence of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. In this work, we propose CityNavAgent, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN. Specifically, we design a hierarchical semantic planning module (HSPM) that decomposes the long-horizon task into sub-goals with different semantic levels. The agent reaches the target progressively by achieving sub-goals with different capacities of the LLM. Additionally, a global memory module storing historical trajectories into a topological graph is developed to simplify navigation for visited targets. Extensive benchmark experiments show that our method achieves state-of-the-art performance with significant improvement. Further experiments demonstrate the effectiveness of different modules of CityNavAgent for aerial VLN in continuous city environments.