AI & ML
Working Through Representation: Malleable Workspaces for Human-Agent Knowledge Work
arXiv cs.HC0 views
arXiv:2610.30893v1 Announce Type: new 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.
Source:https://arxiv.org/abs/2610.30893PreviousAbstract Priorities, Concrete Rewards: How Stated Preferences Relate to What People Reward in AI ResponsesNextYour Home Remembers: A Voice-Enabled Household Memory Assistant for Conversational Retrieval
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