Doc To The Future: Infomorphs for Interactive, Multimodal Document Transformation and Generation
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912929472839680 |
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| author | Kumaravel, Balasaravanan Thoravi |
| author_facet | Kumaravel, Balasaravanan Thoravi |
| contents | Creating new documents by synthesizing information from existing sources is an important part of knowledge work in many domains. This process often involves gathering content from multiple documents, organizing it, and then transforming it into new forms such as reports, slides, or spreadsheets. While recent advances in Generative AI have shown potential in automating parts of this process, they often provide limited user control over the handling of multimodal inputs and outputs. In this work, we introduce the notion of "infomorphs" which are modular, user-steerable, AI-augmented transformations that support controlled synthesis, and restructuring of information across formats and modalities. We propose a design space that leverage infomorph-driven workflows to enable flexible, interactive, and multimodal document creation by combining Generative AI techniques with user intent and desired information context. As a concrete instantiation of this design space, we present DocuCraft, a canvas-based interface to visually compose infomorph workflows. DocuCraft allows users to chain together infomorphs that perform operations such as page extraction, content summarization, reformatting, and generation, leveraging Generative AI at each stage to support rich, cross-document and cross-modal transformations. We demonstrate the capabilities of DocuCraft through an example-driven usage scenario that spans across different facets of common knowledge work tasks illustrating its support for fluid, human-in-the-loop document synthesis and highlights opportunities for more transparent and modular interaction for Generative AI-assisted information work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Doc To The Future: Infomorphs for Interactive, Multimodal Document Transformation and Generation Kumaravel, Balasaravanan Thoravi Human-Computer Interaction Information Retrieval Creating new documents by synthesizing information from existing sources is an important part of knowledge work in many domains. This process often involves gathering content from multiple documents, organizing it, and then transforming it into new forms such as reports, slides, or spreadsheets. While recent advances in Generative AI have shown potential in automating parts of this process, they often provide limited user control over the handling of multimodal inputs and outputs. In this work, we introduce the notion of "infomorphs" which are modular, user-steerable, AI-augmented transformations that support controlled synthesis, and restructuring of information across formats and modalities. We propose a design space that leverage infomorph-driven workflows to enable flexible, interactive, and multimodal document creation by combining Generative AI techniques with user intent and desired information context. As a concrete instantiation of this design space, we present DocuCraft, a canvas-based interface to visually compose infomorph workflows. DocuCraft allows users to chain together infomorphs that perform operations such as page extraction, content summarization, reformatting, and generation, leveraging Generative AI at each stage to support rich, cross-document and cross-modal transformations. We demonstrate the capabilities of DocuCraft through an example-driven usage scenario that spans across different facets of common knowledge work tasks illustrating its support for fluid, human-in-the-loop document synthesis and highlights opportunities for more transparent and modular interaction for Generative AI-assisted information work. |
| title | Doc To The Future: Infomorphs for Interactive, Multimodal Document Transformation and Generation |
| topic | Human-Computer Interaction Information Retrieval |
| url | https://arxiv.org/abs/2602.23366 |