RAG

Peng Peng, Weiwei Lin, Wentai Wu et al.

Proposes HaS, a speculative retrieval method that accelerates RAG systems by leveraging homology-aware caching, reducing latency without accuracy loss in large-scale knowledge retrieval.

Wentao Zhang, Yan Zhuang, ZhuHang Zheng et al.

DEJA exposes stealthy RAG failures that mimic valid responses, forcing a paradigm shift in security evaluation—essential for deploying reliable RAG systems that must detect subtle, non-obvious degradation.

Hyunseok Park, Jihyeon Kim, Jongeun Kim et al.

CHOP reduces RAG hallucinations by iteratively chunking and reassembling documents with LLMs—directly improving factual accuracy in production systems without requiring retraining or new embeddings.

Haoran Lou, Ziyan Liu, Chunxiao Fan et al.

SLQ enables retrieval with frozen MLLMs via shared latent queries—preserving pre-trained knowledge while avoiding costly fine-tuning, a game-changer for scalable, stable multimodal retrieval systems.

Jiahang Lin, Kai Hu, Binghai Wang et al.

Introduces a multi-turn RL agent for visual QA over long documents, enabling iterative retrieval and synthesis—transforming RAG from static lookup to dynamic reasoning for complex document systems.

Sunkyung Lee, Jihye Back, Donghyeon Jeon et al.

Introduces authority-aware generation in retrieval, directly improving trustworthiness in high-stakes domains by biasing LLMs toward credible sources—not just relevance—enabling safer deployment in healthcare and finance.

Sohyun An, Hayeon Lee, Shuibenyang Yuan et al.

FRESCO introduces dynamic evaluation for RAG re-rankers under evolving data, exposing severe performance drops in static benchmarks. Builders must test re-rankers with temporal drift to ensure real-world reliability.

Joongmin Shin, Chanjun Park, Jeongbae Park et al.

MultiDocFusion integrates vision and text to preserve structural context in long industrial documents, dramatically improving RAG accuracy—essential for enterprises relying on precise QA from complex PDFs, manuals, and reports.

Bo Li, Mingda Wang, Gexiang Fang et al.

GRIP turns retrieval into a native decoding action so the model can decide when to search, rewrite queries, and stop inside one reasoning trace instead of bolting on a controller.

Artem Gadzhiev, Andrew Kislov

Synthius-Mem replaces retrieval-heavy agent memory with structured persona memory, improving both long-term recall and adversarial robustness against invented facts.

Kyle Whitecross, Negin Rahimi

RecaLLM tackles the lost-in-thought problem by interleaving reasoning with explicit in-context retrieval, giving long-context models a practical way to stay grounded at up to 128K tokens.

Yucheng Shen, Jiulong Wu, Jizhou Huang et al.

VISOR pushes visual RAG toward real agent behavior with iterative search, evidence-space tracking, and drift control for long-horizon multimodal question answering over documents.

Mohamed Darwish Mounis, Mohamed Mahmoud, Shaimaa Sedek et al.

Shows multimodal retrieval is often a query-alignment problem, not an encoder problem, and beats strong baselines by rewriting image-text queries into retrieval-optimized text.

Nusrat Sultana, Abdullah Muhammad Moosa, Kazi Afzalur Rahman et al.

A careful 40-setting RAG study shows dense retrieval, query reformulation, and reranking matter more than many heavyweight choices, offering practical tuning guidance that extends beyond medical QA.