This research presents a novel Reasoning-Aware Attention Sparsity approach that significantly reduces computational demands during long inference chains.

• Reduces the O(N) time and memory consumption traditionally required for long reasoning chains
• Intelligently identifies and retains only the most critical token information
• Maintains reasoning performance while decreasing computational requirements
• Enables more efficient deployment of LLMs for complex reasoning tasks

This innovation has significant implications for engineering applications that require extensive reasoning, like mathematics and programming, by making LLM deployment more cost-effective and accessible.

Efficient Long-Decoding Inference with Reasoning-Aware Attention Sparsity