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The results show that FootprintRAG helps users compare retrieval directions, revise candidate evidence, recover potentially overlooked evidence, recover potentially overlooked evidence, and trace generated summaries back to supporting evidence units.
Users are largely ignoring the security and privacy information provided by GenAI tools, opting instead for popularity as a proxy, which significantly impacts their willingness to use these tools for sensitive tasks.
Forget generic code generation – this work shows that structure-aware retrieval of domain-specific examples slashes the debugging needed to get LLMs to produce working scientific visualization pipelines.
