arXiv:2606.07878v1 Announce Type: new Abstract: The KV cache is the memory bottleneck of long-horizon language model deployment. Practically, a deployable compactor must be lightweight enough to call during inference, expressive enough to preserve context under constraint, and reusable across a trajectory. Existing compaction methods satisfy only part of this requirement: selection methods are lightweight but subset-bound, while synthesis methods are expressive but rely on per-context optimization. Here we introduce Still, a small per-layer Perceiver trained once against a frozen base model th
The deployment of long-horizon language models faces significant memory bottlenecks, driving the immediate need for efficient KV cache compaction methods.
Efficient KV cache compaction is critical for scaling long-context AI models, impacting the operational costs and practical limitations of advanced AI deployments.
The introduction of 'Still' provides a lightweight, expressive, and reusable compaction method, potentially enabling more efficient and cost-effective deployment of powerful language models.
- · AI model developers
- · Cloud providers
- · AI-powered application developers
- · SaaS companies utilizing large language models
- · Companies with inefficient AI inference infrastructure
- · Hardware providers focused solely on raw memory capacity without efficiency solu
More cost-effective and scalable deployment of long-context large language models.
Accelerated development and adoption of AI applications requiring extensive contextual understanding.
Increased competition among AI service providers as barriers to deploying advanced models are lowered.
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Read at arXiv cs.LG