Figure 5: Word clouds of text prompts for the text-only generation (T2I) task (left) and the multimodal generation task (right). Figure 5 visually summarizes the prominent semantic elements in the benchmark prompts for text-only492 (T2I) and multimodal generation tasks. The differentiation of the word clouds reflects task-specific493 features of MMGen-Bench, emphasizing spatial and descriptive details in T2I tasks, while multimodal494 tasks more frequently involve social and interactive scenarios.495 Aspect Objects Relations Attributes Counting Overall Spearman ω 0.469 0.909 0.601 0.839 0.699 As depicted in Figure 6, the distribution of aspect types differs notably between the text-only497 generation (T2I) and multi-modal generation tasks. In the T2I setting, "Objects" dominate with498 38.3%, while "Attributes" and "Relations" also constitute substantial proportions (33.9% and 25.4%,499 respectively).

Text-to-Image (T2I) models have achieved remarkable success in generating visual content from text inputs.

Table 5: All task variations except shape used in VLMbench. The shape variation of each task can be found in the detail descriptions of each task category. Variations Totals Values Color 25 seen:red, maroon, lime, green, blue,navy, yellow, cyan, magenta, silver, gray, olive, purple, teal, azure, violet, rose, black, white unseen: brown, gold, pink, chocolate, coral Size 5 larger, smaller, large, medium, small Relative Position 5 top, front, rear, left, right Level 3 top, middle, bottom Amount 2 fully, slightly Action Type 2 open, close Table 6: All object models used in VLMbench. The number behind the object class indicate the instance number of that class. Here, we list variations used for these tasks in Table. 5. For each demonstration, all things in the scene will change the pose at the beginning. When building an instance-level task with one variation, the other variations will also randomly change. For example, in the demonstrations of "Pick & Place objects" with "size" variation, all objects' color and relative positions, including targets and distractors, will randomly change. In the dataset, we have five types of objects, shown in Table 6. We will explain each task in detail as follows. Visualizations can be found on the project website. A.1 Pick & Place Objects Task Definition: The agent needs to distinguish the specific object to grasp and then place it into a particular container. The object can be placed anywhere with any orientation inside the container.

Q: For what purpose was the dataset created? Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q: Who funded the creation of the dataset? Q: What do the instances that comprise the dataset represent (e.g., documents, photos, people, Q: How many instances are there in total (of each type, if appropriate)? As shown in Table 1, the dataset statistics are as follows: Grounding Task: 111,770 samples for training, 21,616 samples for testing. For grounding, we use only one annotation per image.

Q: For what purpose was the dataset created? Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Q: Who funded the creation of the dataset? Q: What do the instances that comprise the dataset represent (e.g., documents, photos, people, Q: How many instances are there in total (of each type, if appropriate)? As shown in Table 1, the dataset statistics are as follows: Grounding Task: 111,770 samples for training, 21,616 samples for testing. For grounding, we use only one annotation per image.

This paper presents UrbanKGent, a unified large language model agent framework, for urban knowledge graph construction.