ConvMem: Convolutional Memory for Long-Context Reasoning

Imagine you're building a chatbot that needs to understand long conversations or documents. Traditional large language models (LLMs) can struggle with this because they have fixed limits on how much text they can process at once. When they try to handle long contexts, they often break down, leading to issues like losing track of important details or generating irrelevant responses. This is particularly problematic in scenarios where the conversation or text spans multiple segments, as existing methods like MemAgent can be slow and prone to overfitting due to their reliance on reinforcement learning training.

To tackle these challenges, ConvMem offers a fresh perspective by treating the reasoning process as a hierarchical convolution, similar to how convolutional neural networks (CNNs) operate. Instead of processing text in a linear fashion, ConvMem organizes it into a tree structure, allowing for faster and more efficient reasoning. It employs techniques like Configurable Strides and Skip Connections to enhance the model's ability to capture and propagate relevant information, while Multi-Kernel Convolution helps break down complex queries into manageable parts. This approach not only reduces the risk of errors but also allows for significant parallel processing, making it more efficient than previous methods. Compared to earlier work, ConvMem provides a more scalable and effective solution for handling long contexts without the drawbacks of traditional training methods.

ConvMem introduces a novel hierarchical convolution approach for long-context reasoning.

The experiments show solid performance improvements over training-free baselines.