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Graph Machine: Towards Better Pretraining via Edges

Paper recorded by Signals 4 on 2026-09-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.LG · 机器学习 · first seen 2026-09-03

Abstract

We introduce the Graph Machine (GM), an architecture that maintains an $O(n)$-sized state and accesses it through sparse, dynamic routing. Unlike methods with fixed-size states or sparse but static routing, GM preserves $O(n)$ complexity in its sparse layers without restricting the potentially accessible state size to $O(1)$. Instead, GM uses edges - pointer-like objects updated differentiably by

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#143 most recent of 215 cs.LG papers we have recorded · ↑ newer: A Common Measure of Communication for Speech Brain-Computer Interfaces · ↓ older: GRADSOLVE: fast exact gradients for ODE ensembles on GPUs
Cite this page: Graph Machine: Towards Better Pretraining via Edges: the #143 most recent of 215 cs.LG papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/graph-machine-towards-better-pretraining-via-edges.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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