SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2609.00689v1
• Date: Tue, 01 Sep 2026 04:10:32 GMT
• Title: SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation
• Abstract summary: We propose SCoNE (Selective Context-aware Neuron Editing), a training-free model editing approach that improves retrieval noise robustness.<n>SCoNE requires only a small number of mining samples, no fine-tuning, and no inference-time overhead.
• Score: 14.04209080008843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) is highly sensitive to retrieval noise: when retrieved documents mix informative and irrelevant context, LLMs are easily distracted, leading to hallucinations. To overcome this, we propose SCoNE (Selective Context-aware Neuron Editing), a training-free model editing approach that improves retrieval noise robustness by selectively strengthening context-aware FFN neurons that are identified by both high attribution and high cross-input variability. SCoNE requires only a small number of mining samples, no fine-tuning, and no inference-time overhead. Across various knowledge-intensive question-answering benchmarks and two LLM backbones, SCoNE consistently outperforms competitive baseline methods. Our code is available at https://github.com/HYU-ARK-Lab/SCoNE.

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