Forget Without Compromise: Nexus Sampling for Streaming KV-Cache Eviction Under Fixed Budgets
arXiv:2606.23961v1 Announce Type: new Abstract: Long-context and agentic LLM workloads push the KV cache past any fixed memory budget, forcing the inference stack to permanently evict tokens at every step of a continuous-inference stream. Existing methods all share the same template, a per-step direct-attention score followed by deterministic top-$K$ selection, which converts a single below-cutoff step into an irreversible verdict and permanently erases any subtly important token that direct attention cannot single out from noise. To address this challenge, we propose Nexus Sampling, a trainin
The increasing complexity and context demands of LLMs are pushing the limits of current KV-cache management, making efficient memory utilization a critical bottleneck.
This development proposes a novel approach to KV-cache eviction, potentially enabling more efficient and robust continuous inference for long-context and agentic LLMs.
Current top-K selection methods for KV-cache eviction, which can irreversibly erase important tokens, may be replaced by more nuanced sampling approaches, leading to improved LLM performance and cost efficiency.
- · LLM developers
- · Cloud AI providers
- · AI-powered applications
- · Researchers in AI memory management
- · Providers of less efficient KV-cache solutions
More sophisticated and resource-efficient LLM inference becomes possible for demanding AI applications.
This could accelerate the development and deployment of truly autonomous AI agents by improving their long-term memory and contextual understanding.
Improved LLM efficiency might reduce the overall compute requirements for certain AI tasks, potentially impacting compute hardware demand curves.
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Read at arXiv cs.LG