DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911395223699456 |
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| author | Wang, Anqi Li, Zhengyi Tong, Xin Hui, Pan |
| author_facet | Wang, Anqi Li, Zhengyi Tong, Xin Hui, Pan |
| contents | Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent formulation, while LLMs treat inputs as complete specifications. This challenges design intent formulation, where designers must progressively refine understanding through exploration. Existing tools either sacrifice exploratory flexibility for structural stability or leave reasoning implicit, failing to support human-LLM alignment. Through a formative study with eight designers, we introduce curated reasoning-enabling designers to explicitly inspect, reorganize, and selectively regenerate LLM reasoning structures. We present DesignerlyLoop, implementing this through a two-layer structure separating design intent from LLM reasoning. A study with 20 designers demonstrates that curated reasoning significantly improves design quality and creativity. Our work contributes a novel interaction paradigm for human-LLM alignment, transforming LLMs from content generators into structured reasoning partners in creative design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment Wang, Anqi Li, Zhengyi Tong, Xin Hui, Pan Human-Computer Interaction Recent large language models (LLMs) show promise in design tasks, yet a fundamental misalignment persists: design thinking requires iterative intent formulation, while LLMs treat inputs as complete specifications. This challenges design intent formulation, where designers must progressively refine understanding through exploration. Existing tools either sacrifice exploratory flexibility for structural stability or leave reasoning implicit, failing to support human-LLM alignment. Through a formative study with eight designers, we introduce curated reasoning-enabling designers to explicitly inspect, reorganize, and selectively regenerate LLM reasoning structures. We present DesignerlyLoop, implementing this through a two-layer structure separating design intent from LLM reasoning. A study with 20 designers demonstrates that curated reasoning significantly improves design quality and creativity. Our work contributes a novel interaction paradigm for human-LLM alignment, transforming LLMs from content generators into structured reasoning partners in creative design. |
| title | DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.15331 |