arXiv:2605.28044v1 Announce Type: new Abstract: Cited RAG evaluation often treats visible sources as a grounding signal, but a real, topically relevant citation can still under-warrant the attached wording. We study this diagnostic failure as citation laundering: a related source is presented as warrant for an over-strong claim. We introduce FORCEBENCH, a contrastive stress test for evidence-force calibration. Each item holds a cited passage fixed and pairs an evidence-calibrated claim with a localized force-raised variant across five operational axes: relation, modality, scope, temporal valid
The proliferation of RAG systems highlights the critical need for robust evaluation methods, with current approaches proving insufficient to detect subtle but significant errors.
This research addresses a core vulnerability in AI systems: the potential for citations to mislead, which can undermine trust and foster misinformation.
The proposed 'evidence-force calibration' and FORCEBENCH stress test offer a more granular way to evaluate RAG systems, pushing for greater accuracy beyond mere topical relevance.
- · AI developers focused on explainability and reliability
- · Users of RAG systems (increased trust)
- · Fact-checking organizations
- · AI ethics and safety researchers
- · Developers of RAG systems relying solely on superficial citation metrics
- · Organizations deploying unchecked RAG systems
There will be increased pressure on RAG system developers to integrate more sophisticated evaluation methods.
The improved reliability of RAG outputs could accelerate their adoption in high-stakes fields like law and medicine.
A higher standard for AI-generated factual claims could lead to new regulatory frameworks for AI content generation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI