Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper, we conduct a thorough empirical analysis, focusing on the attention modules across layers, by which we reveal several key insights about how these models process visual data: (i) the internal representation of the query tokens (e.g., representations of "describe the image"), is utilized by the model to store global image information; we demonstrate that the model generates surprisingly descriptive responses solely from these tokens, without direct access to image tokens. (ii) Cross-modal information flow is predominantly influenced by the middle layers (approximately 25% of all layers), while early and late layers contribute only marginally. (iii) Fine-grained visual attributes and object details are directly extracted from image tokens in a spatially localized manner, i.e., the generated tokens associated with specific object or attribute attend strongly to their corresponding regions in the image. We propose novel quantitative evaluation to validate our observations, leveraging real-world complex visual scenes. Finally, we demonstrate the potential of our findings in facilitating efficient visual processing in state-of-the-art VLMs.

Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portraythe samesubject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy persubject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multiple subjects. Here, we presentConsiStory, atraining-freeapproach that enables consistent subject generation by sharing the internal activations of the pretrained model. We introduce a subject-driven shared attention block and correspondence-based feature injection to promote subject consistency between images. Additionally, we develop strategies to encourage layout diversity while maintaining subject consistency. We compareConsiStoryto a range of baselines, and demonstrate state-of-the-art performance on subject consistency and text alignment, without requiring a single optimization step. Finally,ConsiStorycan naturally extend to multi-subject scenarios, and even enable training-freepersonalizationfor common objects.

Modern optimal multi-agent path finding (MAPF) algorithms can scale to solve problems with hundreds of agents. To facilitate comparison between these algorithms, a benchmark of MAPF problems was recently proposed. We report a comprehensive evaluation of a diverse set of state-of-the-art optimal MAPF algorithms over the entire benchmark. The results show that in terms of coverage, the recently proposed LazyCBS algorithm outperforms all others significantly, but it is usually not the fastest algorithm. This suggests algorithm selection methods can be beneficial. Then, we characterize different setups for algorithm selection in MAPF, and evaluate simple baselines for each setup. Finally, we propose an extension of the existing MAPF benchmark in the form of different ways to distribute the agents’ source and target locations.