A Creative Agent is Worth a 64-Token Template

Ruixiao Shi; Fu Feng; Yucheng Xie; Xu Yang; Jing Wang; Xin Geng

cs.CVMar 18, 2026

A Creative Agent is Worth a 64-Token Template

Authors: Ruixiao Shi, Fu Feng, Yucheng Xie, Xu Yang, Jing Wang, Xin Geng

Organizations: School of Computer Science and Engineering, Southeast University, Nanjing, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China

Abstract

Text-to-image (T2I) models have substantially improved image fidelity and prompt adherence, yet their creativity remains constrained by reliance on discrete natural language prompts. When presented with fuzzy prompts such as a creative vinyl record-inspired skyscraper'', these models often fail to infer the underlying creative intent, leaving creative ideation and prompt design largely to human users. Recent reasoning- or agent-driven approaches iteratively augment prompts but incur high computational and monetary costs, as their instance-specific generation makes creativity'' costly and non-reusable, requiring repeated queries or reasoning for subsequent generations. To address this, we introduce CAT, a framework for Creative Agent Tokenization that encapsulates agents' intrinsic understanding of ``creativity'' through a Creative Tokenizer. Given the embeddings of fuzzy prompts, the tokenizer generates a reusable token template that can be directly concatenated with them to inject creative semantics into T2I models without repeated reasoning or prompt augmentation. To enable this, the tokenizer is trained via creative semantic disentanglement, leveraging relations among partially overlapping concept pairs to capture the agent's latent creative representations. Extensive experiments on Architecture Design, Furniture Design, and Nature Mixture tasks demonstrate that CAT provides a scalable and effective paradigm for enhancing creativity in T2I generation, achieving a $3.7\times$ speedup and a $4.8\times$ reduction in computational cost, while producing images with superior human preference and text-image alignment compared to state-of-the-art T2I models and creative generation methods.

Figures & tables

Figure 1: CAT: C reative A gent T okenization for Efficient Creative Generation. CAT generates the token template that directly injects creative intent into fuzzy prompts, enabling efficient, high-quality combinatorial creativity across Architecture Design , Furniture Design , and Nature Mixture tasks.

Figure 2: Breaking the Quality-Efficiency Bottleneck in Creative Generation. Compared with (a) direct generation, (b) think-then-generation and (c) agent-based approaches improve conceptual fusion but incur high time and API costs (see App. A ). In contrast, (d) CAT encapsulates the agent’s intrinsic understanding of “creativity” in a reusable token template that directly enhances semantic representations, delivering superior visual quality with minimal cost and inference time.

Figure 3: Overview of CAT. (a) We introduce a Creative Augmentor to augment fuzzy prompts and a Creative Evaluator to filter generated concepts, with valid ones stored in a Concept Pool . (b) The Creative Tokenizer maps each fuzzy prompt embedding to a corresponding token template, which is directly concatenated with the fuzzy embedding to form a creative embedding for creative generation. (c) The Creative Tokenizer is trained via semantic disentanglement, leveraging relations among partially overlapping concept pairs to capture the agent’s implicit understanding of creativity.

Figure 4: Performance of CAT on the Architecture Design and Furniture Design Tasks . We compare CAT with the representative open-source T2I model FLUX.1 Labs (2024) , proprietary models GPT-Image-1.5 OpenAI (2025) and Gemini 3.1 Flash Image Google (2026) (enhanced with Gemini-3.1-Pro for complex reasoning), as well as agent-driven creative generation methods T2I-Copilot Chen et al. (2025) and CREA Venkatesh et al. (2025) .

Figure 5: Performance of CAT on the Nature Mixture Task. We compare CAT with representative T2I models, including FLUX.1 Labs (2024) and Stable Diffusion 3 (SD3) Esser et al. (2024) , as well as state-of-the-art creative generation methods such as BASS Li et al. (2024) , AGSwap Zhang et al. (2025) , and CreTok Feng et al. (2025b) .

		Architecture Design			Furniture Design			Nature Mixture
		T2I-Copilot	CREA	CAT	T2I-Copilot	CREA	CAT	BASS	AGSwap	CreTok	CAT
(a)	VQAScore $\uparrow$	0.80	0.80	0.85	0.87	0.88	0.91	0.76	0.76	0.85	0.89
	PickScore $\uparrow$	23.51	23.64	24.11	23.29	22.90	23.51	21.64	21.90	22.25	24.03
	ImageReward $\uparrow$	1.19	0.90	1.46	1.14	0.77	1.26	0.70	0.50	1.10	1.77
(b)	VLM-as-a-judge $\uparrow$	7.30	7.82	8.44	7.88	7.64	8.20	6.81	7.50	7.59	8.82
(c)	User Study (Aver. Rank) $\downarrow$	2.33	2.19	1.48	2.28	2.27	1.44	3.16	2.98	2.52	1.34

Table 1: Quantitative Comparison of Creative Generation. Performance is measured using automated image-text and human-aligned metrics, with creativity further evaluated by GPT-4o, and complemented by a user study reporting average human preference ranks.

Figure 6: Ablation Study on the Contribution of Each Loss Component. The baseline uses direct generation with FLUX; Variant #1 uses only $\mathcal{L}_{\text{align}}$ ; Variant #2 adds $\mathcal{L}_{\text{disen}}$ ; CAT corresponds to the full model with $\mathcal{L}_{\text{total}}$ .

Figure 7: Impact of Token Number on Token Template Performance. We evaluate the performance of the token template with 16, 32, 64 (Ours), and 128 tokens.

Figure 8: Token Template vs. Creative Augmentor. Continuous token template encodes richer creative intent, yielding more coherent and visually harmonious outputs than the Creative Augmentor using discrete language prompts.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

	Method	Integration	Alignment	Originality	Aesthetic	Comprehensive
Arch.	T2I-Copilot	6.80 $\pm$ 1.95	7.25 $\pm$ 1.94	6.97 $\pm$ 1.69	8.43 $\pm$ 0.67	7.30 $\pm$ 1.60
	CREA	7.60 $\pm$ 1.89	7.60 $\pm$ 1.70	8.03 $\pm$ 1.42	8.65 $\pm$ 0.75	7.82 $\pm$ 1.39
	CAT	8.45 $\pm$ 1.07	8.60 $\pm$ 0.82	7.83 $\pm$ 0.99	8.85 $\pm$ 0.49	8.44 $\pm$ 0.80
Furni.	T2I-Copilot	7.92 $\pm$ 1.64	8.10 $\pm$ 1.52	7.55 $\pm$ 1.36	8.50 $\pm$ 1.05	7.88 $\pm$ 1.23
	CREA	7.78 $\pm$ 1.44	7.20 $\pm$ 1.70	7.95 $\pm$ 1.65	8.15 $\pm$ 0.67	7.64 $\pm$ 1.17
	CAT	8.22 $\pm$ 1.71	8.18 $\pm$ 1.16	8.07 $\pm$ 1.55	8.50 $\pm$ 0.89	8.20 $\pm$ 1.21

Appendix

Table 2: Detailed Results of VLM-as-a-judge. We reported detailed sub-scores for Integration, Alignment, Originality, and Aesthetic.

Task	Concept	Style
Architecture Design	(10 items): Clock Tower, Museum, Observation Tower, Observatory, Pavilion, Shopping Mall, Skyscraper, Stadium, Suspension Bridge, Windmill	(55 items): Accordion, Armadillo Shell, Bamboo, Bat Wing, Bird Nest, Bookshelf, Bubble, Butterfly, Cactus, Camera Lens, Candle, Caterpillar, Circuit Board, Coral Reef, Corn, Crystal, Dandelion, Deer Antler, DNA Double Helix, Dolphin, Fish, Folding Fan, Gyroscope, Honeycomb, Hourglass, Jellyfish, Lantern, Leaves, Lotus, Mountain, Mushroom, Onion, Origami Crane, Peacock Feather, Peanut, Piano, Pinecone, Pipe Organ, Pocket Watch, Prism, Ribcage, Rose, Sailing Ship, Sea Urchin, Seashell, Shark, Snail Shell, Spider Web, Stained Glass, Sunflower, Tortoise Shell, Tree Branches, Vinyl Record, Walnut, Woven Basket
Furniture Design	(20 items): Bar Stool, Bed, Bookshelf, Cabinet, Chair, Chandelier, Coat Rack, Coffee Table, Cradle, Dining Table, Dresser, Fireplace, Floor Lamp, Mirror, Sofa, Swing, Table Lamp, Television, Tub, Vase	(38 items): Accordion, Armadillo Shell, Bamboo, Bat Wing, Bird Nest, Bubble, Butterfly, Cactus, Circuit Board, Coral Reef, Crystal, Dandelion, Deer Antler, Folding Fan, Honeycomb, Hourglass, Jellyfish, Lantern, Leaves, Lotus, Mushroom, Origami Crane, Peacock Feather, Pinecone, Prism, Ribcage, Rose, Sea Urchin, Seashell, Snail Shell, Spider Web, Stained Glass, Sunflower, Tortoise Shell, Tree Branches, Vinyl Record, Walnut, Woven Basket
Task	Concept
Nature Mixture	(60 items): Ant, Antelope, Bat, Bear, Bee, Broccoli, Butterfly, Cabbage, Cactus, Camel, Carrot, Cat, Cauliflower, Chameleon, Cherry, Chicken, Coconut, Crab, Crocodile, Deer, Dog, Dolphin, Eagle, Elephant, Fox, Frog, Giraffe, Gorilla, Grape, Hippo, Horse, Jellyfish, Kangaroo, Lemon, Lettuce, Lion, Mango, Mushroom, Octopus, Owl, Peacock, Penguin, Persimmon, Pineapple, Potato, Pumpkin, Rabbit, Rhino, Snail, Snake, Spider, Starfruit, Strawberry, Sunflower, Tiger, Tomato, Turtle, Watermelon, Whale, Zebra

Appendix

Table 3: Overview of the Expanded CangJie Dataset. For the Architecture Design and Furniture Design tasks, each pair consists of a primary concept and a style-guiding concept. For the Nature Mixture task, the two concepts in each pair are combined in equal proportion. All items are listed in alphabetical order.

Figure 9: Interface of the User Study. Each volunteer is assigned a subset of questions from three tasks: Architecture Design (20 questions), Furniture Design (20 questions), and Nature Mixture (30 questions). For each participant, 10 questions are randomly selected from each task. For each question, participants rank the presented images based on perceived creativity.

Figure 10: Qualitative results of CAT on the Architecture Design task.

Figure 11: Qualitative results of CAT on the Furniture Design task.

Figure 12: Qualitative results of CAT on the Nature Mixture task.

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