LightMem: Lightweight and Efficient Memory-Augmented Generation

SwiftMem has been introduced as a query-aware agentic memory system designed to enhance the efficiency of large language model (LLM) agents by enabling sub-linear retrieval through specialized indexing techniques. This system addresses the limitations of existing memory frameworks that rely on exhaustive retrieval methods, which can lead to significant latency issues as memory storage expands.

A new method called DyCP (Dynamic Context Pruning) has been introduced to enhance the performance of Large Language Models (LLMs) in long-form dialogues by dynamically segmenting and retrieving relevant memory at query time, improving answer quality while reducing response latency.

The introduction of E^2-LLM (EEG-to-Emotion Large Language Model) marks a significant advancement in emotion recognition from electroencephalography (EEG) signals, addressing challenges such as inter-subject variability and the need for interpretable reasoning in existing models. This framework integrates a pretrained EEG encoder with Qwen-based large language models through a multi-stage training pipeline.