CONF-KV: Confidence-Aware KV Cache Eviction with Mixed-Precision Storage for Long-Horizon LLM
arXiv:2605.24786v1 Announce Type: new Abstract: Long-horizon LLM inference turns the key--value (KV) cache into the dominant GPU memory consumer and makes per-token attention increasingly expensive. Many common eviction policies use static recency windows or historical attention, leaving unused a signal computed on every decoding step: the model's current uncertainty. We introduce CONF-KV, a KV-cache manager that converts the next-token distribution into a scalar confidence score and uses it to choose the per-step cache budget, retaining more context when the model is uncertain and pruning agg
The increasing scale and complexity of LLMs, particularly for long-horizon inference, are making memory management and computational efficiency critical bottlenecks that new solutions like CONF-KV are designed to address.
This development allows LLMs to handle longer contexts more efficiently and cost-effectively, advancing their capabilities and reducing the computational resources previously required for complex tasks.
The ability of LLMs to process and maintain long-term context is significantly enhanced through more intelligent KV cache management, moving beyond static eviction policies.
- · AI developers and researchers
- · Cloud computing providers (reduced memory overhead for LLM hosting)
- · Enterprises leveraging long-context LLMs
- · Developers relying solely on static KV cache management
LLMs can process and generate longer, more coherent texts and engage in more extended dialogues without prohibitive memory or cost.
This improved efficiency could accelerate the adoption of LLMs in applications requiring deep contextual understanding, such as advanced AI agents or comprehensive knowledge assistants.
The reduced computational footprint for long-horizon tasks may democratize access to advanced LLM capabilities, fostering innovation across a broader range of developers and applications.
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Read at arXiv cs.LG