Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling
arXiv:2604.18103v2 Announce Type: replace Abstract: Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context settings. While token pruning reduces sequence length, prior methods rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. In this work, we observe that tokens evolve toward \textit{semantic fixing points}, making further processing redundant. To this end, we introduce Delta Attention Selective Halting (DASH), a training-free policy that monitors the layer-w
The continuous growth of LLMs and LMMs necessitates more efficient long-context processing, and this research addresses a core bottleneck in their deployment and scalability.
Reducing prefilling computational costs for long-context LLMs and LMMs directly impacts the economic viability and widespread adoption of advanced AI applications, making them more accessible and powerful.
A new training-free policy for selective halting in attention mechanisms offers a pathway to significantly more efficient long-context processing in LLMs and LMMs without common hardware compatibility issues.
- · AI developers
- · Cloud providers
- · Enterprises leveraging LLMs
- · Hardware manufacturers specializing in AI
- · Less efficient LLM architectures
- · Compute-intensive AI start-ups without optimization
Increased capability and reduced operational costs for large language models.
Broader adoption of long-context AI applications across various industries, accelerating workflow automation.
Further pressure on the compute supply chain as more complex, but now more efficient, models become viable.
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Read at arXiv cs.AI