# EntropyInfer: An Entropy-Guided Adaptive Inference Framework for Large Models on Long Texts

> EntropyInfer dynamically identifies rigid and dynamic attention heads via attention entropy, enabling head-level and segment-level adaptive computation allocation, and achieves a 2.39x end-to-end speedup on long texts with over 100,000 tokens.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-08T14:02:18.000Z
- 最近活动: 2026-06-09T05:26:04.184Z
- 热度: 133.6
- 关键词: 长文本推理, 注意力熵, KV缓存压缩, 稀疏注意力, 自适应推理, 大语言模型, 推理加速
- 页面链接: https://www.zingnex.cn/en/forum/thread/entropyinfer
- Canonical: https://www.zingnex.cn/forum/thread/entropyinfer
- Markdown 来源: floors_fallback

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## [Introduction] EntropyInfer: An Entropy-Guided Adaptive Inference Framework for Large Models on Long Texts

### Core Information
- **Project Name**: EntropyInfer (Entropy-Guided Adaptive Inference Framework for Large Models on Long Texts)
- **Core Method**: Dynamically identify rigid and dynamic attention heads via attention entropy, enabling head-level and segment-level adaptive computation allocation
- **Main Results**: Achieve a 2.39x end-to-end speedup on long texts with over 100,000 tokens, with minimal quality loss
- **Source & Open Source**: arXiv paper (published on June 8, 2026, link: http://arxiv.org/abs/2606.09508v1), code open-sourced at https://github.com/SHA-4096/EntropyInfer

## Research Background: Efficiency Dilemma of Long Text Inference and Limitations of Existing Methods

### Efficiency Bottlenecks
When large language models process long texts, **attention computation** and **KV cache storage** are the main bottlenecks.

### Flaws of Existing Methods
Sparse attention and KV cache compression methods have the problem of a "one-size-fits-all" strategy:
- Apply the same sparse pattern to all attention heads
- Use a uniform computation budget for different contexts
- Ignore differences in attention behavior between heads and across contexts
Leading to inefficient resource allocation.

## Core Insight: Attention Entropy Reveals Dynamic Characteristics of Heads

### Role of Entropy
Attention entropy measures distribution uncertainty: low entropy (focused on a few positions), high entropy (scattered browsing).

### Two Types of Attention Heads
- **Rigid Heads**: Entropy value close to zero, fixed behavior (e.g., position encoding, syntax marker heads)
- **Dynamic Heads**: Entropy value fluctuates, adjusts focus with context (e.g., semantic content, entity association heads)

### Key Finding
The distribution of head types is context-dependent and cannot be pre-determined offline.

## EntropyInfer Framework: Entropy-Guided Adaptive Inference Strategy

### Prefill Phase
- **Head-Level Allocation**: More resources for high-entropy heads, aggressive compression for low-entropy heads
- **Segment-Level Allocation**: Split long inputs into segments, adjust strategies independently for each segment

### Decoding Phase
- Consider KV cache compression of generated output tokens
- Compress KV cache in latent space to reduce memory usage

## Experimental Evaluation: Significant Speedup and Quality Preservation

### Model Benchmarks
Tested on Llama, Qwen, and openPangu series models.

### Main Results
- **End-to-End Speedup**: Up to 2.39x in scenarios with over 100,000 tokens
- **Baseline Comparison**: Outperforms SnapKV, AdaKV, and CritiPrefill
- **Quality Preservation**: QA accuracy loss <2%, summary ROUGE>98%, code generation Pass@1 shows almost no drop.

## Practical Application Scenarios and Open Source Contributions

### Application Scenarios
- **Long Document Processing**: Legal contracts, academic papers, book summaries
- **Dialogue Systems**: Customer service bots, personal assistants, education tutoring
- **Code Generation**: Code completion, review, document generation

### Open Source Contributions
Code is open-sourced at https://github.com/SHA-4096/EntropyInfer, including core implementation, multi-model adaptation, evaluation scripts, and usage documentation.

## Limitations, Future Directions, and Conclusion

### Limitations
- Entropy computation introduces additional overhead
- Some optimizations depend on specific hardware
- Effectiveness for extreme lengths (million tokens) needs verification

### Future Directions
- Hardware co-design
- Theoretical deepening (link between entropy and model capability)
- Multimodal extension
- Fully adaptive computation system

### Conclusion
EntropyInfer breaks through the efficiency bottleneck of long text inference, realizes intelligent resource allocation by understanding attention behavior, and adaptivity is the future direction.