arXiv:2602.16284v2 Announce Type: replace Abstract: Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typically managed through compaction in token space via summarization. However, summarization can be highly lossy, substantially harming downstream performance. Recent work on Cartridges has shown that it is possible to train highly compact KV caches in latent space that closely match full-context performance, but at the cost of slow and expensive end-to-end optimization. This work describes an approa
The increasing demand for long-context language models in deployed settings is driving innovation in KV cache compaction, as current methods are either lossy or too slow.
Efficient KV cache compaction directly addresses a key bottleneck in scaling large language models, enabling longer context windows with better performance and lower computational cost.
This approach offers a potentially more efficient and less lossy method for managing long contexts in large language models compared to current summarization techniques.
- · AI model developers
- · Cloud providers
- · AI application users
- · Hardware manufacturers for AI inference
- · Companies reliant on highly lossy summarization for long contexts
- · Legacy KV cache management solutions
Improved performance and cost-efficiency for long-context language models.
Accelerated development and broader adoption of AI applications requiring extensive contextual understanding.
Potentially enables new classes of AI agents and complex reasoning systems that critically rely on very long memory.
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Read at arXiv cs.LG