arXiv AI
Jul 28
Design Theater: A Benchmark for Generative UI
arXiv:2607. 22928v1 Announce Type: new Abstract: Generative UI tools promise to democratize UI design by turning natural language descriptions into complete interfaces.
Design-MLLM: A Reinforcement Alignment Framework for Verifiable and Aesthetic Interior Design
arXiv:2603. 13312v2 Announce Type: replace-cross Abstract: Interior design is a requirements-to-visual-plan generation process that must simultaneously satisfy verifiable spatial feasibility and comparative aesthetic preferences.
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arXiv AI
Jul 1
Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist
arXiv:2606. 31711v1 Announce Type: new Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models.
arXiv AI
Jul 8
Foundation Models for Automatic CAD Generation
arXiv:2607. 05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications.
Hugging Face Trending Papers
Jul 8
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.
arXiv Machine Learning
Jun 5
Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding
arXiv:2505. 05026v5 Announce Type: replace-cross Abstract: User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept.
arXiv AI
Jul 9
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
arXiv:2607. 07521v1 Announce Type: cross Abstract: AI agents that generate final answers based on user input often do not meet the needs of creative fields.
arXiv Machine Learning
Jun 26
Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs
arXiv:2606. 26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text.
arXiv AI
Jun 2
Visual Persuasion: What Influences Decisions of Vision-Language Models?
arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).
arXiv AI
Aug 7
Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift
arXiv:2605. 16411v2 Announce Type: replace-cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximization under joint probabilistic modeling.
arXiv AI
Jun 6
MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
arXiv AI
Jun 3
TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
arXiv:2605. 20731v2 Announce Type: replace-cross Abstract: Text-to-image models now generate graphic design at production scale, yet their supervision still comes primarily from photo-style preference datasets with a single overall verdict per comparison.
arXiv AI
Jul 21
Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models
arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.