arXiv:2508.09001v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, inference over extended contexts is bottlenecked by the Key-Value (KV) cache, whose memory footprint grows linearly with sequence length and dominates latency at each decoding step. While recent KV cache compression methods identify and load important few tokens, they focus predominantly on input contexts and fail to address the cumulative attention errors that arise during long decoding. In
The increasing deployment of LLMs in complex, long-context applications like reasoning and multi-turn dialogue is creating an urgent need for more efficient inference methods.
Efficient long-context generation is critical for scaling AI capabilities, reducing operational costs, and enabling more sophisticated AI applications across industries.
This research proposes a method to significantly reduce the memory and latency bottlenecks associated with the Key-Value cache in LLMs, allowing for more practical and powerful long-context applications.
- · Large Language Model developers
- · Cloud AI providers
- · Enterprises using LLMs for complex tasks
- · AI hardware manufacturers
- · Companies with inefficient LLM deployments
- · Legacy AI solutions
Reduced cost and increased capability for current LLM applications requiring long context.
Acceleration of AI agent development and deployment due to enhanced reasoning and memory capacity.
New AI-powered product categories emerge that were previously computationally infeasible, reshaping workflows and industries.
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Read at arXiv cs.LG