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Aug 2026

STaR: a soft-labeling and triplet-aware retriever for efficient retrieval-augmented QA

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. · 0 citations
Jul 2026

QUBO-Optimized Evidence Selection for Retrieval-Augmented Question Answering with Unconventional Solvers

The QUBO selector achieves competitive exact-match and token-F1 performance relative to LLM-based selectors while providing a solver-compatible formulation for structured evidence selection, suggesting that multi-hop evidence selection can be cast as discrete optimization, opening a path toward RAG pipelines where LLMs are reserved for semantic processing and answer generation, while context selection is handled by Ising/QUBO-compatible solvers.

Rahul Singh, Madhav Vadlamani · 0 citations
Jul 2026

MeAI++: Improving answer quality with reinforcement learning for graph retrieval-augmented generation

MeAI++ is proposed, a novel framework that integrates knowledge graph based retrieval with a reinforcement learning (RL) optimization loop to jointly enhance retrieval and generation and confirms the effectiveness and generalizability of MeAI++ for complex, knowledge-intensive question answering.

Tram Nguyen, Truong H. V. Phan · 0 citations
Book Open access Aug 2026

MCoRe: Multi-Entry Complementary Retrieval with Reflection-Guided Iteration for Multi-Hop QA

MCoRe, a multi-entry complementary retrieval framework with reflection-guided iteration for multi-hop QA that enables multi-entry complementary retrieval by indexing entry units at multiple semantic resolutions with explicit links to chunk evidence, and fusing cross-resolution hits via chunk-level voting to form a compact evidence set for answer generation.

Juxiang Zeng, Zhuohui Gao, Zhe Hou et al. · 0 citations

Dynamic Multi-Path Retrieval for Knowledge-based Visual Question Answering

Dynamic Multi-Path Retrieval for KB-VQA (DMRAG) is proposed, which re-trieves candidates through multiple retrieval paths that capture complementary visual and semantic cues and performs Question-Adaptive Gated Fusion to balance contributions from different modalities according to the query’s information need.

Zeyu Song, Yimin Deng, Yuxin Zhang et al. · 0 citations
Open access 2026

SAC-RAG: Semantic Adaptive Context Compression for Retrieval-Augmented Generation

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. · 0 citations

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