RAG

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.