The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise

Recent work has suggested that adding irrelevant documents to the input of retrieval-augmented generation (RAG) systems can improve question-answering performance, a phenomenon referred to as the ``\textit{Power of Noise}.'' This motivated investigations into the role of noise in information retrieval. In this paper, we reproduce the main findings of Cuconasu et al. \cite{cuconasu2024power} and evaluate the robustness of the effect under extended experimental settings. We first confirm that the phenomenon holds under the original setup, which uses earlier-generation LLMs, restrictive prompting and constrained decoding settings. We subsequently introduce a series of extensions to investigate the underlying causes of the noise effect, examining the authors' original design choices including the use of different models, instruction prompting, and relaxed output length constraints. Across these ablations, the Power-of-Noise pattern proves highly sensitive to inference configuration: it can appear, weaken, or disappear under small changes to prompt formulation and decoding limits. Combined with our error analysis, which shows substantial contributions from truncation and malformed generations, this variance indicates that the original effect cannot be robustly confirmed as a general benefit of noisy retrieval under these experimental conditions. More broadly, our work highlights the importance of carefully scrutinizing inference design in retrieval-augmented generation systems. Our code is available at https://github.com/ina0105/The-Power-of-Noise-Reproduction.