2026· Poster Volume 0007 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· 0 citations
TL;DR
DARE-RAG is proposed, an adaptive retrieval framework that activates LLM-based query expansion only for retrieval-challenging queries, and formulates expansion activation as a lightweight binary classification problem using probe retrieval signals, including score margin, variance, entropy, query length, and lexical specificity.
Abstract
Retrieval-Augmented Generation (RAG) systems face a fundamental trade-off: query expansion can improve retrieval effectiveness for ambiguous or underspecified queries, yet indiscriminate expansion introduces unnecessary latency and retrieval noise. Existing RAG pipelines typically apply expansion uniformly, failing to distinguish between easy and retrieval-challenging queries.To address this issue, we propose DARE-RAG, an adaptive retrieval framework that activates LLM-based query expansion only for retrieval-challenging queries. Our method formulates expansion activation as a lightweight binary classification problem using probe retrieval signals, including score margin, variance, entropy, query length, and lexical specificity. A lightweight MLP predicts whether expansion is likely to improve retrieval quality, and expansion is triggered only when the predicted confidence exceeds a percentile-calibrated threshold.DARE-RAG further integrates a dual-path hybrid retrieval architecture combining BM25 sparse retrieval and BGE dense retrieval, fused via Reciprocal Rank Fusion (RRF), followed by a Cross-Encoder reranker for context refinement. Experiments on NQ-Open and HotpotQA demonstrate that DARE-RAG consistently improves retrieval effectiveness and end-to-end QA accuracy while clearly reducing average end-to-end latency compared with corresponding always-expand variants of BM25, BGE-m3, and their RRF-fused hybrid retriever. Extensive ablation studies and efficiency analyses verify the effectiveness of our utility-guided expansion strategy.
Experimental results show that SAC-RAG reduces token consumption by 38%–58% at the cost of only a 1–2 percentage point EM drop, with EM actually improving after compression for reasoning-type questions, achieving the optimal quality–efficiency trade-off in terms of token consumption.
Deyu Zhang, Hongqiang Yu, Jinze Huo et al.· IEEE Access· 0 citations
This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between queries and candidate passages, significantly enhancing retrieval ranking precision and semantic robustness.
Jiali Jiang, Chih-Yung Chang, Youxi Li et al.· Multimedia Systems· 0 citations
The Adaptive Multi-Stage Vector Retrieval (AMSVR) framework is proposed, prioritising weighted, drift-resistant composition over uniform fusion, and offers tailored configurations: AMSVR-Scientific (dense + tuned hybrid) peaks at NDCG@10 = 0.7570 on SciFact, while AMSVR-Full (seven stages) targets broader, noisier corpora where Recall@100 matters most.
Samsudeen Alabi Bankole, Yakub Kayode Saheed· NLP & Big Data· 0 citations
ARF-RAG is proposed, an Adaptive Retriever-Friendly Retriever-Friendly Retrieval-Augmented Generation framework that adopts a role-unified mechanism, in which a single LLM simultaneously performs all retrieval-related and generation actions, including retrieval decision-making, query generation, and answer generation, enabling coherent optimization across all components.
Yubo Fang, Hai-tao Yu, Hideo Joho et al.· International Conference on...· 0 citations
Document Visual Question Answering (DocVQA) often leverages Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large Vision-Language Model (LVLM). Existing approaches typically retrieve a fixed top-$k$ number of pages regardless of query complexity, which increases LVLM latency and may degrade answer accuracy. We introduce ViSAR (Visual Semantic Activation Retrieval), a training-free adaptive-$k$ retrieval method for late-interaction visual document retrieval. ViSAR operates directly in the embedding space to construct a query-conditioned page-level similarity matrix that highlights query-relevant semantics and dynamically determines the number of pages to retrieve. Across multiple encoders and LVLMs, ViSAR retrieves compact, query-adapted page sets that reduce RAG latency by up to 58.7\%, while maintaining or improving answer accuracy compared with fixed top-$k$ and adaptive retrieval heuristics. Furthermore, we show that the similarity matrix structure correlates with answer accuracy, suggesting future directions for retrieval quality-aware document understanding.
Adrien Mialland, Marc Plantevit, Julien Gallois et al.· 0 citations
Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly challenging in multi-hop question answering (QA), where supporting passages are often linked through intermediate entities and relations that must be progressively uncovered. Existing retrieval approaches typically rely on a single retrieval intent or one-shot query expansion, limiting their ability to adapt to newly retrieved evidence and potentially introducing noisy or redundant retrieval signals. To address these limitations, we propose a training-free multi-hop retrieval framework that integrates evidence-conditioned exploration, passage-specific contrastive refinement, and coverage-aware final ranking. During offline indexing, the framework constructs passage-specific contrastive facets that characterize each passage relative to its semantically similar neighbors, providing fine-grained signals to distinguish closely related candidates. At inference time, the framework iteratively retrieves evidence, generates probes targeting unresolved information needs, refines candidate relevance using the contrastive facets, and selects a complementary set of passages that collectively cover diverse evidence-seeking intents. Experiments on MuSiQue, HotpotQA, and 2WikiMultihopQA demonstrate consistent improvements in retrieval quality and downstream QA performance over baselines.
Jungmin Yun, Youngbin Kim· 0 citations
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