Vol. 4 No. 3 (2026), Articles
Vol. 4 No. 3 (2026)

Auditing retrieval self-check for retrieval-augmented answering under 35% distractor density: a factor ablation

Articles
Published 2026-08-31
  • Ross Patterson
  • Chase Johnston
  • Shane Stevenson
Ross Patterson
Chase Johnston
Shane Stevenson
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Keywords

retrieval-augmented answering
retrieval self-check
distractor density
paired simulation
reproducibility

Abstract

We evaluated retrieval self-check for retrieval-augmented answering under 35% distractor density. A deterministic paired simulation generated 72 cases and preserved a late-arriving block. Mean evidence faithfulness changed from 0.545 to 0.599; the paired difference was +0.055 (95% interval +0.052 to +0.057). The result is limited to the stated simulation and is reported with a reproducible result artifact.

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References

Sang, Y. (2025). Robustness of Fine-Tuned Llms Under Noisy Retrieval Inputs. 2025 6th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 417-420. https://doi.org/10.1109/aiea66061.2025.11160531

Maharana, P. R., Verma, A., & Joshi, K. (2025). Retrieval augmented generation for building datasets from scientific literature. https://doi.org/10.26434/chemrxiv-2024-qjx32-v2

Aryani, A. (2024). A Brief Introduction to Retrieval Augmented Generation (RAG). https://doi.org/10.59350/ayxyj-h9751

Shetty, R. (2025). Enhancing Context-Aware Search with Retrieval-Augmented Generation. https://doi.org/10.36227/techrxiv.174060240.09460752/v1

Hou, Y., & Zhang, R. (2024). Enhancing Dietary Supplement Question Answer via Retrieval-Augmented Generation (RAG) with LLM. https://doi.org/10.1101/2024.09.11.24313513

Fasolino, I. (2026). In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning. https://doi.org/10.32388/7hxkqe

Gai, Z., & Kuang, L. (2026). Adaptive Optimization for Retrieval-Augmented Generation via Diagnostic-Driven Strategy Generation. https://doi.org/10.36227/techrxiv.177155637.75354706/v1

Ji, S., Zhang, X., Huang, Y., & Li, D. (2026). CGS-RAG:Community-Aggregated Graph Semantic Retrieval-Augmented Generation. https://doi.org/10.21203/rs.3.rs-9748268/v1

Procko, T., & Ochoa, O. (2024). Graph Retrieval-Augmented Generation for Large Language Models: A Survey. https://doi.org/10.2139/ssrn.4895062

Takamura, T., & Umezawa, A. (2025). Privacy-Preserving Retrieval-Augmented Generation on Local Devices for Regenerative Medicine Applications. https://doi.org/10.1101/2025.10.20.25337146