δ-mem: Efficient Online Memory for Large Language Models
A new paper on arXiv introduces δ-mem, a method for efficient online memory in large language models. The approach compresses past context into a dynamic, updatable memory state, reducing the computational cost of processing long sequences. Unlike traditional attention mechanisms that scale quadratically with sequence length, δ-mem maintains a fixed-size memory that is updated incrementally. Experiments show that δ-mem achieves comparable or better performance on long-document benchmarks while using significantly less memory and compute. The method is particularly relevant for applications like document summarization, multi-turn dialogue, and code generation where context length is critical. The paper includes results on models up to 7B parameters, demonstrating practical scalability. No specific release date or code availability is mentioned.
δ-mem reduces memory costs for long-context LLMs, enabling more efficient deployment.