Robustness depends on whether conclusions remain stable when the data distribution, case mix, prevalence, or operating environment differs from the reported setting. This structured evidence review evaluates "Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation" alongside nine author-disjoint, topically matched publications in reliable retrieval-augmented generation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through robustness under distribution shift, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda uses prespecified shift scenarios, subgroup analysis, calibration checks, and post-deployment monitoring to locate failure boundaries.
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- Hassan, S.-B., Abdullah,, & Abbas, M. (2026). Agentic Self-RAG: Multi-Agent Reasoning for Self-Correcting Retrieval-Augmented Generation. 2026 International Conference on IT and Industrial Technologies (ICIT), 1-6. https://doi.org/10.1109/icit68548.2026.11577708 DOI
- Fasolino, I. (2026). In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning. . https://doi.org/10.32388/7hxkqe DOI
- Samal, M.-P. (2026). A Theoretical Analysis of Self-Contained Retrieval-Augmented Generation with Small Language Models. . https://doi.org/10.36227/techrxiv.177219989.96070478/v1 DOI
- Pi, W. (2024). Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG). . https://doi.org/10.59350/q2pq3-0fv85 DOI
- Xue, X., Zhang, G., Jiang, L., & Liu, C. (2025). A Comparative Study of Retrieval-Augmented Generation, Graph Retrieval-Augmented Generation, and Fine-Tuned Large Language Models for Fire Engineering Knowledge Retrieval. . https://doi.org/10.2139/ssrn.5316632 DOI
- Bose, R. (2025). Introduction to Retrieval-Augmented Generation (RAG). Mastering Retrieval-Augmented Generation, 3-32. https://doi.org/10.1007/979-8-8688-1808-0_1 DOI
- Zhai, W. (2025). SAM-RAG: An Self-adaptive Framework for Multimodal Retrieval-Augmented Generation. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8. https://doi.org/10.1109/ijcnn64981.2025.11227819 DOI
- Gudipati, S.-K., Mishra, N.-A., Ankam, G., Akkisetti, S.-R., Mohammed, A., & Ponugoti, S. (2026). CityCopilot-X: A Real-Time Explainability Panel for Retrieval-Augmented Generation (RAG). 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1-9. https://doi.org/10.1109/icaic67076.2026.11395791 DOI
- Kau, A. (2024). Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models. . https://doi.org/10.59350/xcq3s-jvk04 DOI
- Journal
- Machine Intelligence & Responsible Systems
- Volume
- 1 (2026)
- Article number
- mi20260021
- License
- CC BY 4.0
