The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them of agency and ownership, and readers are concerned about spending their time on text generated by soulless machines. On the other hand, AI-assistance can improve writing as long as writers can conform to publisher policies, and as long as readers can be assured that a text has been verifed by a human. We argue that a system that captures the provenance of interaction with an LLM can help writers retain their agency, conform to policies, and communicate their use of AI to publishers and readers transparently. Thus we propose HaLLMark, a tool for visualizing the writer's interaction with the LLM. We evaluated HaLLMark with 13 creative writers, and found that it helped them retain a sense of control and ownership of the text.

Spatial data is best analyzed in situ, but existing mixed reality technologies can be bulky, expensive, or unsuitable for collaboration. We present VisTorch: a handheld device for projected situated analytics consisting of a pico-projector, a multi-spectrum camera, and a touch surface. VisTorch enables viewing charts situated in physical space by simply pointing the device at a surface to reveal visualizations in that location. We evaluated the approach using both a user study and an expert review. In the former, we asked 20 participants to first organize charts in space and then refer to these charts to answer questions. We observed three spatial and one temporal pattern in participant analyses. In the latter, four experts---a museum designer, a statistical software developer, a theater stage designer, and an environmental educator---utilized VisTorch to derive practical usage scenarios. Results from our study showcase the utility of situated visualizations for memory and recall.