arXiv:2510.00361v2 Announce Type: replace-cross
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.
Bioethical considerations in deploying mobile mental health apps in LMIC settings: insights from the MITHRA pilot study in rural India
IntroductionIn India, untreated depression among women contributes significantly to morbidity and mortality, underscoring an urgent need for accessible and ethically grounded mental health interventions. Mobile