TL;DR
This work formalizes LLM interface augmentation as the mediation strategy framework GLANCE, that defines an interpretive task context, an evidence derivation procedure operating on prompt–response artifacts, and visual encodings that externalize the resulting evidence.
Visual analytics (VA) enables sensemaking through interactive visualization, but effective analysis often requires experts to translate high-level intents into long sequences of interface operations and iteratively interpret visual feedback. We study whether modern vision-language models (VLMs) can take on this role as...
InsightToast is introduced, a mixed-initiative application that monitors verbal discourse in real time, identifies topics and informational needs as they emerge, and proactively retrieves relevant information through a multi-agent large language model (LLM)-based pipeline integrating retrieval-augmented generation (RAG...
MUSE is presented, an interactive meta-agent that enhances user understanding and control of agentic data science systems by dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details.
This study reflects a comprehensive, semantically based, and user-complementary approach to the methodology of the development of intelligent conversational systems and next-generation interactive AI interfaces.
When interacting with large language models (LLMs), users have limited visibility and control over how their personal information is retained, particularly after incorporation into model training. Although some deployed LLMs provide options to opt-out of training, these mechanisms do not offer guarantees about the dele...
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