The Future of Ultra-Long Context Architectures
zephyr_z9 · x · 2026-07-17
This repost discusses the architectural evolution of ultra-long context: K3 has already crossed the 1 million context length threshold, and DeepSeek's sparse attention has reached the same scale.
The author asks what the next step is: sequence lengths of 5M, 10M, or even more. They argue that fixed-state linear attention, particularly methods like GDN/KDA, is highly competitive for long-context scenarios. Hybrid architectures balance scalability with extrapolation to longer sequences, and pairing them with NoPE for positional handling makes things even smoother. The author envisions future hybrid architectures fusing linear / sparse / full attention to break through bottlenecks in long-horizon agent tasks.
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