arXiv:2605.22884v1 Announce Type: new Abstract: Autoregressive Transformer KV caches grow linearly with context length; sliding-window caching bounds memory but discards evicted tokens entirely, so relevant evidence outside the window becomes inaccessible. We introduce \emph{Tensor Cache}, a two-level cache that pairs sliding-window softmax attention as a first-level cache (L1) with a fixed-size outer-product fast-weight memory as a second-level cache (L2) fed by KV pairs evicted from the window. Recent tokens remain in exact local attention; evicted pairs are compressed into a per-layer matri
The continuous drive for more efficient and performant Transformer models necessitates novel architectural solutions to address limitations like KV cache growth.
This development could significantly improve the context length capabilities and efficiency of Transformer models, impacting the scalability of large language models and other AI applications.
Transformer models could become more memory-efficient and capable of handling longer contexts without incurring proportional memory costs, allowing for more complex tasks and deeper understanding.
- · AI model developers
- · Cloud providers
- · AI researchers
- · Less efficient memory caching techniques
Increased practical context windows for large language models will become more common.
AI agents and other applications requiring vast contextual memory will see performance and capability improvements.
The development of novel AI architectures might slow as existing Transformer models become more robust and less resource-constrained.
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Read at arXiv cs.LG