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

ArXi:2510.00361v2 Announce Type: replace-cross 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.