C lue A nchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025)

Abstract: Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the retrieved documents, failing to extract and integrate the key clues needed to support faithful and interpretable reasoning, especially in cases where relevant evidence is implicit, scattered, or obscured by noise. To address this issue, we propose ClueAnchor, a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization. ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations, optimizing the model by selecting the most appropriate reasoning path for the given context through reward-based preference optimization. Experiments show that ClueAnchor significantly outperforms prior RAG baselines in the completeness and robustness of reasoning. Further analysis confirms its strong resilience to noisy or partially relevant retrieved content, as well as its capability to identify supporting evidence even in the absence of explicit clue supervision during inference. All codes are available at https://github.com/thunlp/ClueAnchor.

## introduction large language models (llms) have achieved remarkable progress across a wide range of nlp tasks @xcite @xcite . however, their reliance on static training data often results in knowledge gaps and hallucinations. retrieval-augmented generation (rag) mitigates this limitation by incorporating external evidence to enhance factual accuracy @xcite . yet, since llms are not explicitly trained to utilize retrieved content, which animal's experiment provided crucial support for the human moon landing mission? ... they often struggle to effectively incorporate and reason over external information @xcite . to address this, recent work has focused on optimizing generation models to better leverage retrieved content. @xcite improves faithfulness via multi-task instruction tuning, while rag-ddr @xcite further improves performance by sampling multiple candidate responses and optimizing toward those with higher reward signals. however, their success require the model to construct correct reasoning paths. as shown in figure 1 , relevant evidence can be implicit, disturbed by semantically similar noise, or scattered across multiple passages. existing rag methods are frequently misled by irrelevant content, failing to establish robust links between retrieved evidence and the answer. this reveals a limitation of rag systems: even when relevant information is retrieved, models may perform reasoning over incomplete or misaligned content @xcite @xcite . motivated by this finding, we hypothesize that the ground-truth answer can serve as a backward signal to confirm correct information, thus uncovering key clues buried in the retrieved documents and facilitating the reconstruction of a correct reasoning path. building on this intuition, we propose clueanchor, a novel framework that enhances rag through clue-anchored knowledge reasoning exploration and optimization. the proposed framework consists of two components. the knowledge reasoning exploration (kre) module first predicts a key clue from retrieved documents conditioned on the ground truth and generates multiple reasoning paths under three configurations: internal reasoning without external context, external reasoning grounded in the retrieved evidence, and clueanchored reasoning guided by the predicted clue. the knowledge reasoning optimization (kro) module evaluates these candidate paths using taskspecific reward signals, and finetunes the model via preference optimization to favor the most effective one. by combining clue-aware generation with reward-guided path selection, clueanchor enables the model to identify key clues from noisy retrieved documents and use them as anchor for reasoning, thereby improving evidence grounding and producing more coherent and faithful outputs. our experiments results demonstrate that clueanchor outperforms all baseline models, achieving an improvement of more than 3.6% compared to the previous state-of-the-art method @xcite . this improvement highlights clueanchor's ability to substantially improve answer generation quality, affirming the effectiveness of its overall design under challenging retrieval conditions. further analysis shows that clueanchor maintains stable performance as retrieval noise increases, indicating strong robustness to imperfect evidence and an enhanced ability to focus on key clues despite distracting content. moreover, clueeanchor generalizes beyond supervised clues, effectively identifying relevant information during inference without explicit clue guidance. in addition to retrieval quality, the effectiveness of rag also depends on how well the generation model utilizes retrieved content @xcite . prompting strategies like chain-of-note @xcite guide attention to relevant context, while retrieval-aware fine-tuning @xcite explicitly trains models to integrate external knowledge. other methods, such as differentiable data rewards @xcite , address conflicts between parametric and retrieved knowledge to enhance factual consistency and reduce hallucinations. these strategies @xcite collectively align model outputs with external evidence, improving both performance and trustworthiness. another active research direction aims to improve transparency and reasoning quality by modeling intermediate steps. methods such as chainof-thought @xcite , decomposition @xcite , and selfrefinement @xcite guide models to explicitly articulate their reasoning. @xcite introduces "thought trajectories" to trace answer formation. in rag, approaches like radcot @xcite distill structured reasoning for greater efficiency and interpretability. these demonstrate that making reasoning explicit improves factuality and verifiability @xcite @xcite . while prior work has improved faithfulness and rationale alignment in rag @xcite , most approaches assumes relevant evidence is al-ready available to the model and focuses on aligning generation accordingly @xcite . in contrast, we address a more fundamental challenge: the model may still overlook critical evidence due to its implicit nature or low salience. we propose the clueanchor framework, which explicitly highlights clue signals from noisy retrievals to guide more grounded and interpretable reasoning. ## method this section introduces the clueanchor framework, which is illustrated in figure 2 . we first describe the preliminaries of clueanchor (section 3.1), and then detail its clue-anchored knowledge reasoning exploration and knowledge reasoning optimization process (section 3.2). ## experimental methodology this section details our experimental setup, covering datasets, metrics, baselines, and implementation details. more experimental details are provided in the appendix a.1 and a.3. dataset. to evaluate our approach, we construct training and evaluation sets from diverse qa benchmarks. all datasets use passages retrieved from wikipedia (izacard et al., 2022) via the bge-largeen-v1.5 retriever @xcite . the training set spans various reasoning paradigms, including open-domain qa (nq @xcite , triviaqa @xcite ), multi-hop qa (hot-potqa @xcite , 2wikimqa @xcite ), and reading comprehension (squad @xcite ). evaluation covers ten datasets, including five in-domain test sets and five out-of-domain benchmarks-searchqa @xcite , popqa @xcite , beerqa @xcite , webquestions @xcite , and musique @xcite , which reflect diverse knowledge and reasoning styles, from commonsense to complex multi-hop. evaluation metrics. we adopt accuracy as the primary evaluation metric across all qa tasks, following previous work @xcite @xcite . baselines. we evaluate a range of rag methods under a unified setup, spanning from parametric llms to retrieval-augmented approaches incorporating reasoning, instruction tuning, and reward optimization. vanilla llm relies solely on internal knowledge. vanilla rag @xcite and replug @xcite enhances query generation by incorporating retrieved passages through in-context learning. ra-dit (lin et al., 2023) applies multi-task instruction tuning to better utilize retrieved passages. radcot @xcite augments reasoning ability by distilling chain-ofthought rationales from a teacher model. rag-ddr @xcite leverages differentiable data rewards by sampling multiple candidate responses and optimizing toward those with higher reward signals. for fair comparison, all methods share the same fixed retriever to isolate improvements from generation modeling. implementation details. we adopt llama-3.1-8b-instruct @xcite and qwen2.5-7b-instruct @xcite as backbone models, fine-tuned with lora @xcite for efficient adaptation. all models are trained for one epoch with a learning rate of 5e-5, using ten retrieved passages as external input during both training and inference. for fair comparison, retrieval-related settings follow @xcite , and all baselines are kept consistent. following prior studies on structured reasoning (guo et al., 2025), we constrain model outputs to a template format: reasoning steps are enclosed @xmath0. . . </@xmath1. . . </@xmath2. ## results and analysis in this section, we first evaluate the overall performance of clueanchor and conduct ablation studies to assess the impact of each component. we then examine its ability to utilize knowledge under different evidence conditions and its robustness to noisy retrieval. finally, we analyze the model's ability to attend to key clues, with case studies provided in appendix a.10. ## conclusion this paper presents clueanchor, a novel framework that enhances retrieval-agmented generation by anchoring reasoning on key evidence clues extracted from retrieved documents. it combines knowledge reasoning exploration with knowledge reasoning optimization to improve the model's ability to identify and leverage critical information. experimental results demonstrate that clueanchor maintains robust performance under increasingly noisy retrieval conditions. further analysis shows that it effectively learns to trace and utilize rele-vant clues during inference, even without access to ground-truth supervision.