TriAttention: Revolutionizing Memory Use in Large Language Models

Extended reasoning in large language models has often hit a stumbling block: memory bottlenecks caused by KV cache. Traditional methods try to assess key-value importance using attention scores but falter due to position rotations, resulting in poor selection and unstable reasoning.

The Pre-RoPE Solution

Enter TriAttention. This new approach bypasses the pitfalls of post-RoPE by analyzing pre-RoPE space. Here, Q and K vectors remain concentrated around stable, non-zero centers. These centers reveal a preference for certain key distances, determined by a trigonometric series. By tapping into these insights, TriAttention estimates key importance based on position preference and additional norm signals.

Performance Metrics

The numbers tell a different story for TriAttention. On the AIME25 benchmark with a 32K-token generation, it matches Full Attention's accuracy. Yet, it delivers 2.5 times the throughput and reduces KV memory by a remarkable 10.7 times. Compared to existing baselines, which achieve only about half the accuracy for similar efficiency, that's a major shift.

Why It Matters

Why should this breakthrough grab your attention? Well, TriAttention allows models like OpenClaw to operate on a single consumer GPU, something previously impossible without running out of memory. This opens doors for deploying sophisticated models without the need for exorbitant computing resources.

strip away the marketing and you get a method that not only maintains accuracy but also vastly improves efficiency. Isn’t that what progress is all about?