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arXiv · 2510.00361

Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers

Hita Kambhamettu·Alyssa Hwang·Philippe Laban·Andrew Head
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Abstract

AI answer engines are a relatively new kind of information search tool: rather than returning a ranked list of documents, they generate an answer to a search question with inline citations to sources. But reading the cited sources is costly, and citation links themselves offer little guidance about what evidence they contain. We present attribution gradients, a technique to boost the informativeness of citations by consolidating scent and information prey in place. Its first feature is bringing evidence amounts, supporting/contradictory excerpts, links to source, contextual explanation into one place. Its second feature is the ability to unravel second-degree citations in place. In a lab study we demonstrate usage of the full gradient in a critical reading task and its support for deep engagement that increased the depth of what readers took away from the sources versus a standard citation and document QA design.

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Hita Kambhamettu, Alyssa Hwang, Philippe Laban, Andrew Head. 2025-10-01. Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers. https://arxiv.org/abs/2510.00361

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