Citations

TL;DR for operators A RAG system can return the right answer, attach a source that genuinely supports that answer, and still leave one critical question unresolved: did that source actually influence the model’s answer generation? A mechanistic study of Llama-3.1-8B-Instruct finds that inline citation behavior is not controlled by one dedicated citation feature. It emerges from a distributed sequence of attention heads and MLPs that includes early entity enrichment, matching between document and question entities, mid-layer processing, and late aggregation that shifts the model toward emitting a citation marker rather than ending the sentence.1 ...

TL;DR for operators A correct multimodal answer with citations can still have a weak evidence trail. In human evaluation on Video-MMMU, Gemini-3-Pro reached 86% question-answering accuracy but only 16.3 on MuRGAt-Score, meaning answer performance and fact-level grounding ranked very differently. MuRGAt evaluates two requirements separately. Coverage asks whether verifiable claims receive citations. Attribution asks whether the cited frames, audio, figures, or temporal segments are actually relevant to and sufficient for the factual claims they accompany. The combined score rewards systems only when both conditions hold. ...