G-MemLLM: Gated Latent Memory Augmentation for Long-Context Reasoning in Large Language Models

Understand

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, yet they remain constrained by the finite capacity of their context windows and the inherent difficulty of maintaining long-term factual consistency during multi-hop reasoning.

• While existing methods utilize context compression or recurrent tokens, they often suffer from ``context rot'' or the dilution of information over long horizons.
• In this paper, we propose \textbf{G-MemLLM}, a memory-augmented architecture that integrates a frozen LLM backbone with a trainable \textbf{Latent Memory Bank}.
• Our key innovation is a GRU-style gated update logic that allows the model to selectively update, preserve, or overwrite latent memory slots, preventing the vanishing gradients of knowledge common in recurrent systems.