Disentangling Retrieval vs. Generation Failures Underlying Unsupported Claims

Disentangle the relative contributions of retrieval failures (citing irrelevant videos) and generation failures (producing unfaithful claims from relevant videos) to unsupported video-grounded outputs generated by the Gemini 2.5 Pro multimodal generative search system, and identify the underlying reasons why certain generated claims depart from the cited source content.

Background

Because the study is a black-box audit of Gemini 2.5 Pro's end-to-end behavior without access to internal mechanisms, it cannot separate errors arising from the retrieval stage from those arising during generation.

Understanding whether unsupported claims primarily result from citing irrelevant sources or from unfaithful synthesis of relevant sources is critical for diagnosing and improving system reliability.

References

This means we cannot determine why certain claims depart from source content, nor disentangle retrieval failures (citing an irrelevant video) from generation failures (producing an unfaithful claim from a relevant video).

— Auditing the Reliability of Multimodal Generative Search  (2604.00944 - Sahneh et al., 1 Apr 2026) in Discussion — Limitations

rEDMRec's student can emit a short explanation grounded in retrieved memory (Section~\ref{sec:method-student}), but this paper evaluates ranking quality, not whether the emitted explanation is faithful to the memory it cites; a human evaluation of explanation faithfulness and plausibility, as outlined in our evaluation plan, is left to future work.

— rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation  (2608.18952 - Nguyen et al., 19 Aug 2026) in Discussion and Limitations, subsection “Explanation faithfulness”

Ongoing work explores dynamic context filtering, entailment-aware generation, hallucination detection mechanisms, and training objectives that better align factual grounding with semantic fluency, but a major open problem is the absence of unified, fine-grained frameworks that jointly model retrieval quality, hallucination detection, and generation-time factual verification; most systems still treat retrieval and generation as loosely coupled components, limiting their ability to assess evidence reliability or correct misleading context during inference.

— Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning  (2610.01936 - Sunil et al., 1 Oct 2026) in Section 5.2, “Hallucination and Reliability Issues”