A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zhuowan Li, Cheng Li, Mingyang Zhang, et al.
Compares RAG and long-context LLMs, finding long-context outperforms RAG but at higher cost, and proposes Self-Route to route queries based on model self-reflection.
Jinyang Wu, Feihu Che, Mingkuan Feng, et al.
Defines seven noise types in RAG, builds NoiserBench benchmark, and shows some noise can benefit LLMs while other noise harms them.
Fei Wang, Xingchen Wan, Ruoxi Sun, et al.
Astute RAG adaptively elicits internal knowledge from LLMs and iteratively consolidates it with external sources to overcome imperfect retrieval in RAG.
Alexandria Leto, Cecilia Aguerrebere, I. Bhati, et al.
This paper investigates how retrieval accuracy affects RAG pipeline performance for QA tasks, finding that lowering search accuracy minimally impacts downstream quality while improving speed and memory.
Zhengren Wang, Jiayang Yu, Dongsheng Ma, et al.
RARE decouples knowledge storage from reasoning by externalizing domain knowledge to retrievable sources and internalizing reasoning patterns, enabling lightweight models to surpass GPT-4 and DeepSeek-R1 by ~20% accuracy.
Kaiwen Wei, Rui Shan, Dongsheng Zou, et al.
MIRAGE enhances test-time scaling for medical QA by combining multi-path parallel inference with structured knowledge graph retrieval to reduce error accumulation and improve traceability.
Orion Weller, Michael Boratko, Iftekhar Naim, et al.
This paper proves that single-vector embedding models have a fundamental dimension-dependent limit on the number of distinct top-k subsets they can represent, and shows this limitation arises even in simple realistic queries.
Dan Gusfield
A comprehensive textbook on string algorithms, suffix trees, and sequence alignment with applications in computational biology.
Liang Hong, Xiao Sun, Yunlei Sun, et al.
This review outlines deep learning methods for automatic text feature extraction, contrasting them with traditional handcrafted approaches.
Zackary Rackauckas
Evaluates RAG-Fusion combining RAG and reciprocal rank fusion for product information retrieval, finding accurate answers but occasional off-topic responses.
Zongxi Li, Zijian Wang, Weiming Wang, et al.
A systematic survey of Retrieval-Augmented Generation (RAG) in education, covering workflow, retrievers, generation optimization, and applications.
Jakub Swacha, Michał Gracel
A survey of 47 papers on RAG chatbots in education, analyzing their character, target support, knowledge scope, LLM, and evaluation.