CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering

2026-07-27

AI Summary

This work addresses the ambiguity in the relationship between cited evidence and supporting claims in long-form question answering, particularly the issue of “evidence boundary overflow.” To resolve this, the authors propose CAGE, a two-stage framework that first constructs an answer-centric cognitive attribution graph to explicitly align semantic units with supporting documents, thereby transforming implicit claim–document matching into structured subgraphs. In the second stage, citation generation is guided by this graph through map-based reasoning. By integrating a plug-in cognitive map induction module with a structured citation inference model, CAGE effectively narrows the attribution space, enhancing both citation faithfulness and claim verifiability. The approach achieves state-of-the-art performance on ASQA, ELI5, and ExpertQA, demonstrating substantial improvements in citation accuracy.

Abstract

Long-form question answering increasingly relies on retrieved evidence to make LLM outputs verifiable, with inline citations tracing claims to source documents. However, existing systems often attach citations that are topically related but insufficient to support their claims. We identify attribution ambiguity as a structural challenge: end-to-end generation must implicitly resolve combinatorial claim--document assignments, obscuring evidential boundaries and increasing the risk of evidence-boundary overrun, where claims exceed cited support. To address this challenge, we propose CAGE (Cognitive Attribution Graphs for Citation Generation), a two-stage framework that introduces an explicit cognitive attribution map before answer generation. CAGE first trains a plug-and-play Cognitive Map Induction Model to construct answer-centered support subgraphs, aligning each semantic answer unit with supporting documents through explicit relations. A Structured Citation Reasoning Model then realizes these units as sentence-level claims with map-aligned citations. Experiments on ASQA, ELI5, and ExpertQA show that CAGE achieves state-of-the-art performance, demonstrating the effectiveness of attribution-space contraction and map-guided citation generation.

Problem

• attribution ambiguity
• inline citation
• evidence-boundary overrun
• long-form question answering
• claim--document alignment

Innovation

• Cognitive Attribution Graphs
• Inline Citation Generation
• Attribution Ambiguity
• Evidence-Boundary Overrun
• Map-Guided Reasoning