Multi-step retrieval-augmented generation has attracted increasing attention due to its capacity to improve the factuality of large language models with iterative retrieved knowledge. However, the performance of multi-step RAG systems is susceptible to potential retrieval noise and fabricated documents in real-world scenarios. Current approaches usually utilize supervised fine-tuning on predetermined noisy contexts to enhance the robustness. However, their performance remains inadequate when it comes to more complicated long-context scenarios due to the lack of adaptability. Towards this end, we propose a novel framework named Context-attended Adversarial Reinforcement Learning (CARE) for multi-step RAG systems against attacks. The core of our CARE is to conduct reinforcement learning on adversarial samples which are alternatingly enhanced with text gradients. In particular, our CARE includes a reward model to identify the accuracy of responses, which is minimized for the generation of adversarial samples with text gradients. These context-attended noisy samples are then utilized for reinforcement learning to maximize the rewards. The whole framework is conducted alternatingly from easy to hard samples to ensure the smoothness of the optimization. Extensive experiments on multi-step RAG benchmark datasets are conducted to validate the superiority of our proposed CARE in multiple noisy scenarios. Our code is available at https://github.com/yingtaoren/CARE.

This study proposes a novel, scalable, non-invasive and channel-independent approach for early dementia detection, particularly Alzheimer’s Disease (AD), by representing Electroencephalography (EEG) microstates as symbolic, language-like sequences. These representations are processed via text embedding and time-series deep learning models for classification. Developed on EEG data from 1001 participants across multiple countries, the proposed method achieves a high accuracy of 94.31% for AD detection. By eliminating the need for fixed EEG configurations and costly/invasive modalities, the introduced approach improves generalisability and enables cost-effective deployment without requiring separate AI models or specific devices. It facilitates scalable and accessible dementia screening, supporting timely interventions and enhancing AD detection in resource-limited communities.