Graph Architecture Replaces Transformer Attention, Extrapolating to 4 Million Tokens in Linear Time
· techtimes.com · popularity 28/100 · 0 similar stories today · AI & Data Science (Artificial Intelligence)
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Summary
GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length extrapolation from 512 training tokens to 4 million tokens in linear O(N) memory on a commodity GPU. Researcher Elisabeth Fink posted the preprint to arXiv on October 27, 2026, with implications for agentic AI long-context inference.
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