Schemex: Discovering Structural Abstractions from Examples
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913016976506880 |
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| author | Wang, Sitong Menon, Samia Li, Dingzeyu Ma, Xiaojuan Zemel, Richard Chilton, Lydia B. |
| author_facet | Wang, Sitong Menon, Samia Li, Dingzeyu Ma, Xiaojuan Zemel, Richard Chilton, Lydia B. |
| contents | Creative and communicative work is often underpinned by implicit structures, such as the Hero's Journey in storytelling, design patterns in software, or chord progressions in music. People often learn these structures from examples - a process known as schema induction. However, because schemas are abstract and implicit, they are difficult to discover: shared structural patterns are obscured by surface-level variation, and balancing generality with specificity is challenging. We present Schemex, an interactive AI workflow that systematically supports schema induction by decomposing it into three tractable stages: clustering examples, abstracting candidate schemas, and contrastively refining them by generating new instances and comparing against originals. Studies show that Schemex produces more actionable schemas than a frontier baseline without sacrificing generalizability, with participants uncovering deep and nuanced structural patterns. We also discuss design implications for the cognitive role of interactive process in structure discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Schemex: Discovering Structural Abstractions from Examples Wang, Sitong Menon, Samia Li, Dingzeyu Ma, Xiaojuan Zemel, Richard Chilton, Lydia B. Human-Computer Interaction Creative and communicative work is often underpinned by implicit structures, such as the Hero's Journey in storytelling, design patterns in software, or chord progressions in music. People often learn these structures from examples - a process known as schema induction. However, because schemas are abstract and implicit, they are difficult to discover: shared structural patterns are obscured by surface-level variation, and balancing generality with specificity is challenging. We present Schemex, an interactive AI workflow that systematically supports schema induction by decomposing it into three tractable stages: clustering examples, abstracting candidate schemas, and contrastively refining them by generating new instances and comparing against originals. Studies show that Schemex produces more actionable schemas than a frontier baseline without sacrificing generalizability, with participants uncovering deep and nuanced structural patterns. We also discuss design implications for the cognitive role of interactive process in structure discovery. |
| title | Schemex: Discovering Structural Abstractions from Examples |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.11795 |