mlsanityData to 14 Oct 2026

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

arXiv 2510.00361 ↗First posted 1 Oct 2025cs.HC cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 58 references that are in the map. First posted on arXiv 1 Oct 2025.

Builds on

31 of 58 references.

References · 31

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
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19 more
  1. 2022Scim: Intelligent Skimming Support for Scientific Paperscited by 27
  2. 2024Supporting Sensemaking of Large Language Model Outputs at Scalecited by 27
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Overseeing Agents Without Constant Oversight: Challenges and Opportunitiescited by 3
  2. 2026From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Outputcited by 0
  3. 2026Understanding Generative AI-mediated User Engagement with Academic Library Resourcescited by 0