Cognitive-inspired xLSTM for multi-agent information retrieval.

In the era of big data and complex information retrieval tasks, multi-agent systems play a crucial role in handling large-scale, complex queries across various domains. Traditional approaches, such as BERT, RoBERTa, and Transformer models, have been widely used in information retrieval. However, these methods often suffer from computational inefficiencies and limited coordination between agents when dealing with long-term dependencies and collaborative tasks. These limitations lead to suboptimal retrieval accuracy and increased processing time, especially in multi-agent environments. To address these challenges, we propose a cognitive-inspired xLSTM model, specifically designed for multi-agent information retrieval. The model introduces advanced memory mechanisms, shared memory structures, and dynamic gating functions, enabling effective long-term dependency management and enhanced agent collaboration. The xLSTM allows agents to exchange information efficiently, optimizing both retrieval speed and accuracy. Extensive experiments on four benchmark datasets-HotpotQA, APPS, MBPP, and FEVER-demonstrate that xLSTM significantly outperforms six state-of-the-art methods in terms of training time, inference time, and key performance metrics such as accuracy, recall, and F1 score. The proposed method not only improves retrieval performance but also enhances computational efficiency, making it a valuable solution for real-time, large-scale information retrieval tasks.