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Open access Jul 2026

Robust and explainable Retrieval-Augmented Generation under retrieval noise

Testing how retrieval noise affects RAG and whether reranking, citation-aware generation, and lightweight verification can improve system behaviour suggests that robust and explainable RAG is a multi-objective problem.

Shirui Chen · 0 citations
Book Open access Jul 2026

Incentivizing Retrieval-Augmented Generation via Inner Adaptive Context Selection

Retrieval-Augmented Generation (RAG) techniques have emerged as a promising direction to merge the non-parametric knowledge into Large Language Models (LLMs), thereby alleviating factual errors, hallucinations and outdated knowledge. Existing RAG methods, which append multiple retrieved documents or passages to the input of LLMs, will inevitably increase the context length, resulting in not only significant computational overhead and inference latency, but also performance degradation. Although reranking or compression modules have been introduced to address these challenges, they overlook the contextual preferences of the generative LLMs itself and may inadvertently discard information that is crucial for generation accuracy. To this end, we introduce InnerRAG, which incentivizes RAG via Inner Adaptive Context Selection. InnerRAG is a novel paradigm that empowers LLMs to autonomously select relevant context during generation. Our proposed InnerRAG endows the model to accurately identify the documents that are most helpful for generation from long contexts. By endowing the model with this capability, InnerRAG facilitates more effective exploitation of external knowledge without being misled by disturbed information, leading to substantial improvements in generation quality while maintaining computational efficiency. Extensive experiments across multiple benchmarks and human evaluations demonstrate that our method consistently outperforms state-of-the-art RAG baselines. Moreover, our framework is orthogonal and complementary to in-context RAG approaches, offering further performance improvements when combined.

Chenxu Cui, Lin Shen, Haihui Fan et al. · 0 citations
Book Open access Jul 2026

Latent Retrieval Augmented Generation

Retrieval-augmented generation (RAG) has emerged as a promising solution to enhance the reliability of large language models (LLMs) with external knowledge. Existing RAG methods operate in explicit representation spaces: in-context methods inject knowledge through text tokens in the input, while parametric methods like Parametric RAG encode documents into model parameters. Although effective, these approaches face inherent limitations. In-context injection suffers from quadratic computational complexity with context length and degraded performance in complex reasoning tasks. Parametric injection, while reducing inference costs, requires substantial storage overhead and computationally expensive offline preprocessing. More fundamentally, both paradigms rely on explicit discrete representations tokens or parameters that may introduce information bottlenecks and hinder seamless knowledge integration. To address these challenges, we introduce Latent RAG, a novel paradigm that performs knowledge injection entirely within the continuous latent space. Our approach encodes documents into ultra-compact latent representations through an offline compression phase, and directly fuses them with the LLM's hidden states via a learned injection mechanism during inference. By operating in the semantic latent space rather than explicit token or parameter spaces, Latent RAG enables more natural knowledge integration while achieving 9,200X storage reduction compared to Parametric RAG. Experimental results on multiple RAG benchmarks demonstrate that Latent RAG substantially enhances both effectiveness and efficiency. Furthermore, it can be seamlessly combined with existing in-context and parametric methods to achieve even better performance.

Shuran Zhou, Junan Chen, Rui Ling et al. · 0 citations
Preprint Aug 2026

Better Retrieval, Worse Robustness: How Multi-hop RAG Amplifies Upstream ASR Errors

Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline as a fixed upstream constraint. We empirically test whether two extensions to standard retrieval-augmented generation (RAG), entity-graph linking and iterative reformulation, absorb or amplify these errors. Using four English accents synthesized through neural TTS, we evaluate four RAG configurations on three multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA and MuSiQue) against a clean-text oracle. Although the structurally richer configurations generally retain higher absolute F1 under ASR input, both extensions amplify the error: the F1 gap from clean text to the highest-WER accent is 36-67% larger under their combination than under naive dense retrieval, on all three benchmarks. The dominant failure mode is corruption of one or more query entities, accounting for 87-96% of degradation cases on 2WikiMultiHopQA across all four methods. Two lightweight surface-form mitigations leave most of the gap intact, indicating that downstream retrieval structure amplifies remaining entity errors. We release code and data at https://github.com/Continuum-AI-Corp/spoken-multihop-rag .

Zhenghua Bao · 0 citations
Preprint Aug 2026

When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading. Existing RAG systems often apply a fixed trust policy toward retrieved evidence, which can either over-trust incorrect context or underuse context when the user explicitly asks for context-following behavior. Therefore, we propose Intent-Guided Decoding (IGD), a framework that arbitrates between retrieved context and parametric memory according to user intent. IGD uses answer-level filtering and token-level correction to steer the final decoding trajectory between retrieved context and parametric memory. We evaluate IGD on three faithful QA benchmarks and three factual-conflict benchmarks across five LLMs, IGD substantially improves factual recovery, achieving gains of up to 65.4 percentage points on factual-conflict benchmarks over Direct RAG, while preserving or improving strict context-following behavior, this findings highlight the importance of balancing factuality and faithfulness in RAG.

Haolin Jin, Pengyue Yang, Hua-Min Chen · 0 citations

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