arXiv:2505.23666v3 Announce Type: replace Abstract: The per-token cost of transformer inference scales with context length, preventing its application to lifelong in-context learning. Linear attention is an efficient alternative that maintains a constant memory footprint, even on infinite context lengths. While this is a potential candidate for lifelong learning, it falls short in memory capacity. In this paper, we propose LoLA, a training-free augmentation to linear attention that boosts associative recall. LoLA distributes past key-value pairs from context into three memory systems: (i) rece
The continuous growth in context window demand for large language models necessitates new architectural approaches to address memory and computational scaling limitations.
Improving the efficiency and memory capacity of linear attention could unlock lifelong in-context learning, a critical bottleneck for more capable and autonomous AI systems.
This research introduces a method to significantly enhance the associative recall of linear attention models without additional training, potentially extending the practical limits of AI context windows.
- · AI model developers
- · Cloud computing providers
- · Enterprises adopting large language models
- · Inefficient transformer architectures
AI models will be able to process and recall information from significantly longer contexts more efficiently.
This improved long-term memory could lead to more adaptive and context-aware AI agents capable of sustained, complex tasks.
The reduced computational cost per token for extended contexts might lower the barrier to entry for developing and deploying advanced AI, impacting the competitive landscape.
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Read at arXiv cs.CL