# AMS KV Compression: Resolving KV Cache Bottlenecks in Long-Context Inference via Region-Aware Quota Allocation

> This article introduces the AMS (Adaptive Mass-Segmented) KV compression framework, which replaces global Top-k selection with region-aware quota allocation to solve the "region erasure" problem in long-context inference and can be seamlessly integrated into inference frameworks like vLLM.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-22T03:32:52.000Z
- 最近活动: 2026-05-25T04:21:09.370Z
- 热度: 61.0
- 关键词: KV缓存压缩, 长上下文推理, 大语言模型, 注意力机制, 推理优化, vLLM, 机器学习系统, 模型部署
- 页面链接: https://www.zingnex.cn/en/forum/thread/ams-kv-kv
- Canonical: https://www.zingnex.cn/forum/thread/ams-kv-kv
- Markdown 来源: floors_fallback

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## AMS KV Compression Framework: A New Solution to KV Cache Bottlenecks in Long-Context Inference

# AMS KV Compression Framework: A New Solution to KV Cache Bottlenecks in Long-Context Inference
Long-context inference is a key requirement for large language model applications, but the linear growth of KV cache limits efficiency. Existing global Top-k compression methods cause the "region erasure" problem (important continuous reasoning blocks are discarded entirely). The AMS (Adaptive Mass-Segmented) framework replaces global Top-k with region-aware quota allocation to solve region erasure, can be seamlessly integrated into inference frameworks like vLLM, and improves inference quality and memory efficiency.

Original author team: Paper author team (arXiv submission)
Source: arXiv (May 22, 2026)
Original link: http://arxiv.org/abs/2605.23200v1

## Background: KV Cache Challenges and Region Erasure Problem in Long-Context Inference

## KV Cache Dilemma in Long-Context Inference
In autoregressive generation, KV cache stores key-value vectors of previous tokens to avoid repeated computation, but increasing context length leads to linear cache growth: a model with 100K context may occupy tens of GB of memory, and frequent access increases latency, limiting batching and concurrency capabilities. Existing solutions use global Top-k to select important tokens, but have fatal flaws.

## Region Erasure Problem
Global Top-k causes "region erasure": in tasks like mathematical reasoning and code generation, logically related tokens form continuous reasoning blocks (e.g., derivation steps, code functions). Global Top-k may discard these blocks entirely, breaking the reasoning chain, losing logical coherence, and reducing output quality. The reason is that global Top-k assumes equal competition among tokens, ignoring the importance of regional structure—individual token importance cannot be evaluated out of context.

## Methodology: Core Innovations and Technical Implementation of the AMS Framework

## Core Idea of the AMS Framework
Shift from token-level competition to region-aware quota allocation:
1. Attention quality distribution analysis: adaptively partition the KV cache;
2. Region-level quota guarantee: key reasoning segments get memory quotas;
3. EMA smoothing mechanism: prevent boundary jitter.

## Technical Implementation
1. **Adaptive Partitioning**: Divide regions at "valleys" of attention quality score distribution to retain high-attention-quality regions;
2. **Region Quota Allocation**: Each region gets a minimum guaranteed quota, with the remaining allocated by importance—tokens within a region share a quota pool;
3. **EMA Smoothing**: Apply time-domain smoothing to region boundaries to stabilize the decoding process.

## Universality and Compatibility
- Orthogonal to existing scorers (TOVA, Expected Attention, etc.), responsible for region division and quota allocation;
- Compatible with paged KV service frameworks like vLLM, supports efficient gather-and-compact execution, no additional steady-state attention overhead, and no need to reconstruct infrastructure.

## Evidence: Experimental Validation of the AMS Framework's Effectiveness

## Experimental Task Validation
Validated effectiveness across multiple task sets:
- **Mathematical Reasoning**: Reduced inference failures caused by region erasure and improved accuracy in MATH500, AIME, and GSM8K tasks;
- **Code Generation**: Preserved key variable definitions, function calls, and logic flow, resulting in more coherent and executable code;
- **Open-Domain QA**: Retained key document information blocks and improved answer accuracy;
- **Sparse Retrieval**: Enhanced precision for retrieving specific information from long documents.

## Advantages Over Baselines
Compared to global Top-k methods:
- Structural Integrity: Continuous reasoning blocks are better preserved;
- Reasoning Coherence: Higher success rate for multi-step reasoning;
- Compression Efficiency: Better performance at the same compression ratio;
- Stability: More stable and consistent decoding outputs.

## Conclusion and Recommendations: Practical Value and Future Directions of AMS

## Practical Significance and Deployment Advice
**When to Use AMS**:
1. Long-context inference (>32K);
2. Structured generation tasks (mathematical reasoning, code generation, etc.);
3. Scenarios requiring high reliability;
4. Existing KV compression infrastructure needs performance improvement.

**Deployment Notes**:
1. Low computational overhead for region division;
2. Need to tune the number of regions and quota ratio;
3. Can be combined with KV quantization for further memory compression.

## Technical Insights
System design must consider data structure characteristics: global optimization easily breaks structure, while structure-aware optimization can compress efficiently while maintaining performance—this is insightful for model pruning, knowledge distillation, etc.

## Limitations and Future Directions
**Limitations**: Region division heuristics are not optimal, task-dependent, and boundaries need adjustment for dynamic attention patterns;
**Future**: Learning-based region division, task-adaptive quota allocation, and combination with sparse attention.

## Conclusion
AMS provides a practical and efficient solution for long-context inference. By solving the region erasure problem via region-aware quota allocation and being compatible with existing frameworks, it is an option for upgrading production environments. As models move toward longer contexts, structure-aware compression technology will become increasingly important.