# V-CAST: Curvature-Aware Spatio-Temporal Pruning Method for Efficient Video Large Language Models

> V-CAST proposes a training-free, plug-and-play Token pruning strategy for video large language models. Through a curvature-guided temporal allocation mechanism and a dual-anchor spatial selection mechanism, it maintains 98.6% of the original performance while reducing peak memory and total latency to 86.7% and 86.4% of the Qwen3-VL-8B-Instruct baseline, respectively.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-29T11:53:32.000Z
- 最近活动: 2026-03-31T01:53:03.099Z
- 热度: 117.0
- 关键词: 视频大语言模型, Token压缩, 时空剪枝, 曲率感知, 视觉Token, 视频理解, 多模态模型, 推理优化, Qwen3-VL, MRoPE
- 页面链接: https://www.zingnex.cn/en/forum/thread/v-cast-fce8e8e1
- Canonical: https://www.zingnex.cn/forum/thread/v-cast-fce8e8e1
- Markdown 来源: floors_fallback

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## V-CAST: Guide to Curvature-Aware Spatio-Temporal Pruning Method for Efficient Video Large Language Models

V-CAST proposes a training-free, plug-and-play Token pruning strategy for video large language models. Through a curvature-guided temporal allocation mechanism and a dual-anchor spatial selection mechanism, it maintains 98.6% of the original performance while reducing peak memory and total latency to 86.7% and 86.4% of the Qwen3-VL-8B-Instruct baseline, respectively, solving the Token explosion problem.

## Efficiency Challenges of Video Large Language Models and Dilemmas in Token Compression

### Efficiency Challenges
VideoLLMs perform strongly across multiple scenarios, but the massive volume of video data leads to Token explosion, resulting in a large context during the pre-filling phase and a sharp increase in computation and memory overhead.

### Dilemmas in Token Compression
Limitations of existing methods: Coarse-grained frame-by-frame allocation ignores content dynamics; scene segmentation easily causes information fragmentation; Token merging leads to MRoPE coordinate misalignment, affecting spatio-temporal reasoning.

## Core Innovations of V-CAST: Curvature Guidance and Dual-Anchor Selection

### Curvature-Guided Temporal Allocation
Model Token compression as trajectory approximation, using curvature to reflect content changes: identify high-curvature semantic turning points, perceive event boundaries, and dynamically allocate Token budgets (fewer allocations for smooth segments, more for intense segments).

### Dual-Anchor Spatial Selection
Preserve high-entropy visual regions without interfering with attention, maintain the original spatio-temporal coordinates of Tokens, and avoid coordinate misalignment.

## Experimental Results of V-CAST: Balance Between Accuracy and Efficiency

### Accuracy Preservation
Achieves 98.6% of the original performance across multiple tasks, with an average improvement of 1.1% over the second-best method.

### Efficiency Improvement
Peak memory is reduced to 86.7% of the baseline, and total latency to 86.4%.

### Cross-Architecture Compatibility
Training-free and plug-and-play, applicable to VideoLLMs of different architectures and scales.

## Practical Application Value of V-CAST

Facilitates:
- Real-time video analysis (low latency supports real-time responses);
- Edge device deployment (reduces memory usage);
- Long video processing (avoids Token explosion);
- Cloud cost optimization (improves efficiency and reduces costs).

## Limitations and Future Directions of V-CAST

### Limitations
Curvature calculation incurs additional preprocessing overhead.

### Future Directions
Optimize curvature calculation, explore synergy with fine-tuning, integrate audio cues, and support dynamic resolution input.

## Conclusion: V-CAST Promotes Efficient Deployment

V-CAST balances accuracy and efficiency through innovative mechanisms, and its training-free, plug-and-play feature allows for rapid application, paving the way for the practical deployment and scaling of video understanding technologies.