ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
arXiv:2602.03203v2 Announce Type: replace Abstract: Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length grows, the key-value (KV) cache expands linearly, incurring significant memory and computation costs. Existing KV cache eviction methods mitigate this issue by discarding less important KV pairs, but often fail to capture complex KV dependencies, resulting in performance degradation. To better balance efficiency and performance, we introduce ForesightKV, a training-based KV cache eviction framewo
The rapid development and widespread adoption of large language models are exposing critical performance bottlenecks related to memory and computation, driving innovation in optimization techniques.
This research addresses a fundamental limitation in the scalability of advanced AI models, directly impacting their commercial viability and the complexity of tasks they can undertake.
Improved KV cache management will allow LLMs to handle much longer reasoning traces more efficiently, reducing operational costs and enabling more sophisticated AI applications.
- · AI model developers
- · Cloud providers
- · Companies deploying LLMs
- · Inefficient AI architectures
- · Hardware providers unprepared for new optimization demands
More complex and capable AI agents become economically feasible due to reduced operational costs.
This efficiency gain precipitates a faster rollout of AI-powered services across various industries, accelerating market consolidation for leading AI providers.
Increased accessibility to advanced reasoning models could democratize AI development, but also intensify competition for compute resources.
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Read at arXiv cs.CL