AutoRecLab is a Python-based tool that automates recommender-systems experiments from natural-language prompts, using retrieval-augmented generation (RAG) and static type verification. It can derive experiment requirements, build and validate prototypes, and expand into full experiments.

Researchers proposed a multimodal visual retrieval-augmented generation (RAG) system for ophthalmology clinical question answering and decision support. The system, Oph-Guid-RAG, integrates query decomposition, rewriting, retrieval, reranking, and multimodal reasoning to provide traceable outputs with guideline page references. Evaluations on HealthBench show improved performance compared to GPT-5.2 and GPT-5.4, indicating the effectiveness of combining vision-based retrieval with controllable reasoning in clinical AI applications.

Researchers proposed a two-stage framework to automate the construction of a business semantic layer from raw application logs using a hierarchical Large Language Model (LLM) and Retrieval-Augmented Generation (RAG). This approach improves semantic quality, reduces maintenance effort, and filters out noise, enabling continuous quality assurance.

Researchers developed an AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) for evidence-based genetic disease severity classification. The agent achieved 93.55% accuracy and provides reliable, automated classification supported by direct evidence. This system enables standardized panel design for genomic screening.

A new benchmark, REASONS, is introduced to evaluate the reliability of scientific citation attribution in large language models (LLMs). The benchmark assesses LLMs' ability to correctly attribute citations and abstain from providing incorrect information when uncertain. The evaluation framework includes various settings, such as zero-context and retrieval-augmented prompting, to test LLMs' performance. The results show that certain configurations can significantly improve citation attribution accuracy, but also highlight the challenges of balancing reliability and responsiveness.

Researchers compared Knowledge Graph-based augmentation and Retrieval Augmented Generation (RAG) for culturally-related question answering. Graph-RAG, which uses a knowledge graph built from Wikipedia articles, showed competitive results to standard RAG and improved the base LLM's accuracy by 72-78%. This has implications for the development of more inclusive and explainable AI models.

Researchers introduce DRAG, a query-adaptive framework for selecting retriever-generator configurations in Retrieval-Augmented Generation (RAG) systems, leading to improved effectiveness-efficiency trade-off. The framework includes two approaches: DRAG_QPP, a training-free routing method using query performance prediction, and DRAG_SFT, a supervised routing method that fine-tunes a large language model to predict retriever-generator configurations.

A study on repairing stale KV caches in retrieval-augmented generation and agentic systems after document edits, proposing a budgeted recomputation approach that outperforms other repair methods.

A new framework called RegRet enhances region-level retrieval in Large Multimodal Models (LMMs) for tasks like e-commerce product search. It leverages a Region-Aware Encoder and multi-stage training pipeline to capture detailed regional features.