# ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models Source: [ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models]() Collection index: [llms.txt]() Data updated: 2026-01-14T05:00:12.520Z Authors: Huipeng Ma, Luan Zhang, Dandan Song, Linmei Hu, Yuhang Tian, Jun Yang, Changzhi Zhou, Chenhao Li, Yizhou Jin, Xudong Li, Meng Lin, Mingxing Zhang, Shuhao Zhang Published: 2026-01-12T06:57:31.000Z ## Abstract In multi-hop reasoning, multi-round retrieval-augmented generation (RAG) methods typically rely on LLM-generated content as the retrieval query. However, these approaches are inherently vulnerable to knowledge overshadowing - a phenomenon where critical information is overshadowed during generation. As a result, the LLM-generated content may be incomplete or inaccurate, leading to irrelevant retrieval and causing error accumulation during the iteration process. To address this challenge, we propose ActiShade, which detects and activates overshadowed knowledge to guide large language models (LLMs) in multi-hop reasoning. Specifically, ActiShade iteratively detects the overshadowed keyphrase in the given query, retrieves documents relevant to both the query and the overshadowed keyphrase, and generates a new query based on the retrieved documents to guide the next-round iteration. By supplementing the overshadowed knowledge during the formulation of next-round queries while minimizing the introduction of irrelevant noise, ActiShade reduces the error accumulation caused by knowledge overshadowing. Extensive experiments show that ActiShade outperforms existing methods across multiple datasets and LLMs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-hop Reasoning on MuSiQue IRCoT 500 samples (test)](): Multi-hop Reasoning; MuSiQue IRCoT 500 samples (test); ACC: 25.59. - [Multi-hop Reasoning on HotpotQA IRCoT (500 samples) (test)](): Multi-hop Reasoning; HotpotQA IRCoT (500 samples) (test); ACC: 54.6. - [Multi-hop Reasoning on 2WikiMQA IRCoT 500 samples (test)](): Multi-hop Reasoning; 2WikiMQA IRCoT 500 samples (test); ACC: 52.8. ## Original sources and implementation [Original paper]() [PDF]()