---
title: "STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting — Stuff That Spins"
description: "arXiv:2607.06614v1 Announce Type: new Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:04:49.909158+00:00"
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# STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.06614  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.06614v1 Announce Type: new Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks. This paper presents STAGformer, a Spatio-Temporal Agent Graph Transformer that achieves efficient global modeling with linear computational complexity. The model introduces a two-step agent attention mechanism, where a small set of le

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