[UPDATE] I Built a O(NlogN) attention system that retains 97% accuracy over long context (MQAR) [P]
ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.
Proof1 community thread
Key points
- It takes less memory and SCALES much better VRAM with tokens
- I’ve also been testing tiny stories and it looks promising
- However i can’t post any presentable results due to lack of funding and resources
Sources (1)
- [1][UPDATE] I Built a O(NlogN) attention system that retains 97% accuracy over long context (MQAR) [P]r/MachineLearning (top, daily) · Oct 13, 11:52 AM
ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.
It takes less memory and SCALES much better VRAM with tokens
Extractive summary: sentences quoted from the sources.
Before this
- Oct 13, 2026inclusionAI/Ling-3.1-flash
- Oct 13, 2026black-forest-labs/flux-3-image-klein-comfy
- Oct 13, 2026Hybrid Reasoning Agents
- Oct 12, 2026[AINews] Meta Muse Wonder & Wonder Full: MSL’s Claude 5.5-tier Frontier Model
- Oct 12, 2026Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB
- Oct 12, 2026AReA: Adaptive Receptive-Field Attention for Efficient High-Resolution Diffusion Transformers