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arXiv · 2610.30893

Working Through Representation: Malleable Workspaces for Human-Agent Knowledge Work

Nan Chen·Long Ling·Luna K. Qiu·Zilong Wang·Yuqing Yang
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Abstract

Generative AI supports knowledge work, yet conversational histories may not capture the evolving organization that guides subsequent action. We introduce the Malleable Workspace, a conceptual model of how humans and agents form and revise a Work Representation and use its organization to advance work within a wider Work Environment. We developed it iteratively through conceptual analysis, comparison of 58 papers, and the construction and three-month longitudinal study of MalleableSpace with seven experienced agent users. Systems varied in what they represented as units and relations. System-assembled context was common; more systems supported local incorporation of agent outputs than reorganization, and fewer supported consequential agent-driven changes beyond the representation. The field study showed how representational roles and organization developed in use. These observations refined the model's account of how representation-grounded action depends on and can change the Work Environment. We contribute a conceptual and analytic foundation for representation-centered human--agent design.

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Nan Chen, Long Ling, Luna K. Qiu, Zilong Wang, Yuqing Yang. 2026-10-22. Working Through Representation: Malleable Workspaces for Human-Agent Knowledge Work. https://arxiv.org/abs/2610.30893

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