TriAttention trims KV cache for long reasoning

Strong idea, but it lives or dies on how broadly that pre-RoPE concentration pattern holds outside the paper’s benchmark set. If it generalizes, this is the kind of cache trick that could make long-context reasoning fit on far smaller GPUs without retraining the base model.

• –Shifts importance scoring away from unstable post-RoPE queries and into a trigonometric model of pre-RoPE Q/K concentration
• –The plug-and-play angle matters: it targets inference bottlenecks, not model re-training
• –The reported 10.7x KV-memory reduction is meaningful for agentic workloads where context, instructions, and tool traces stack up fast
• –Offline calibration plus hand-built priors may limit portability across architectures, domains, or future model families
• –Compared with simpler eviction baselines, the pitch is better reasoning retention under the same cache budget rather than just smaller memory use