arXiv:2605.23200v1 Announce Type: new Abstract: The linear growth of the Key-Value (KV) cache is a critical bottleneck in long-form LLM inference. Existing KV compression methods mitigate this by evicting tokens based on importance scores. However, we show that their reliance on global Top-k selection triggers Region Wipe-out: the severe eviction of contiguous reasoning blocks that derails logical coherence. To address this, we propose Adaptive Mass-Segmented (AMS) KV Compression, a framework that shifts the paradigm from token-level competition to region-aware quota allocation. AMS adaptively
The increasing demand for long-context applications in large language models has pushed existing KV cache compression methods to their limits, necessitating new approaches to maintain logical coherence.
Efficient long-context reasoning is crucial for the continued advancement and practical application of LLMs, directly impacting their commercial viability and utility across many sectors.
This research introduces a novel compression technique that moves beyond token-level eviction to region-aware quota allocation, potentially enabling more stable and coherent long-form LLM inference.
- · LLM developers
- · AI software companies
- · Cloud providers offering AI services
- · Inefficient KV cache designs
- · Competitors using older compression methods
Improved performance and reduced computational costs for long-context LLM applications.
Accelerated development of more complex and reliable AI agents and autonomous systems.
Broader adoption of AI in industries requiring extensive contextual understanding, leading to new AI-powered workflows.
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Read at arXiv cs.LG