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[2602.13680] AllMem: A Memory-centric Recipe for Efficient Long-context Modeling
Summary
The paper presents AllMem, a memory-centric architecture designed to enhance the efficiency of long-context modeling in large language models (LLMs) by integrating Sliding Window Attention with memory networks.
Why It Matters
As LLMs face challenges with long-sequence tasks due to memory and computational constraints, AllMem offers a solution that not only improves performance but also reduces resource requirements. This advancement is crucial for applications requiring extensive context processing, making it highly relevant in the rapidly evolving AI landscape.
Key Takeaways
- AllMem integrates Sliding Window Attention with memory networks for efficient long-context modeling.
- The architecture significantly reduces computational and memory overhead during inference.
- Empirical evaluations show near-lossless performance on long-sequence tasks with reduced resource usage.
- Memory-Efficient Fine-Tuning allows existing LLMs to adopt the AllMem framework easily.
- The approach mitigates issues of catastrophic forgetting in long-context applications.
Computer Science > Artificial Intelligence arXiv:2602.13680 (cs) [Submitted on 14 Feb 2026] Title:AllMem: A Memory-centric Recipe for Efficient Long-context Modeling Authors:Ziming Wang, Xiang Wang, Kailong Peng, Lang Qin, Juan Gabriel Kostelec, Christos Sourmpis, Axel Laborieux, Qinghai Guo View a PDF of the paper titled AllMem: A Memory-centric Recipe for Efficient Long-context Modeling, by Ziming Wang and 7 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) encounter significant performance bottlenecks in long-sequence tasks due to the computational complexity and memory overhead inherent in the self-attention mechanism. To address these challenges, we introduce \textsc{AllMem}, a novel and efficient hybrid architecture that integrates Sliding Window Attention (SWA) with non-linear Test-Time Training (TTT) memory networks. \textsc{AllMem} enables models to effectively scale to ultra-long contexts while mitigating catastrophic forgetting. This approach not only overcomes the representation constraints typical of linear memory models but also significantly reduces the computational and memory footprint during long-sequence inference. Furthermore, we implement a Memory-Efficient Fine-Tuning strategy to replace standard attention layers in pre-trained models with memory-augmented sliding window layers. This framework facilitates the efficient transformation of any off-the-shelf pre-trained LLM into an \textsc{AllMem}-based architecture. Empiric...
