FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/fast-efficient-and-effective-long-horizon-forecasting-for-large-scale-spatial-temporal-graphs-via-mixture-of-experts

stale

Proof freshness: stale
Proof status: partial
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID fast-efficient-and-effective-long-horizon-forecasting-for-large-scale-spatial-temporal-graphs-via-mixture-of-experts | Route /signal-canvas/fast-efficient-and-effective-long-horizon-forecasting-for-large-scale-spatial-temporal-graphs-via-mixture-of-experts

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/fast-efficient-and-effective-long-horizon-forecasting-for-large-scale-spatial-temporal-graphs-via-mixture-of-experts

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

PDF: https://arxiv.org/pdf/2601.05174v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

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Watch and verify: FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

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Watchwatch

Subject: FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

Verdict

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Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
unlocks one-week-ahead (672 steps at a 15-minute granularity) prediction with thousands of nodes.
Implicationpartial
This is a core capability highlighted in the abstract and elaborated in the analysis.
Verificationpartial
partial
Evidencepartial
an adaptive graph agent attention mechanism is proposed to alleviate the computational burden inherent in conventional graph convolution and self-attention modules when applied to large-scale graphs.
Implicationpartial
This is explicitly stated as a key innovation in the abstract and detailed in the analysis.
Verificationpartial
partial
Evidencepartial
we propose a new parallel MoE module that replaces traditional feed-forward networks with Gated Linear Units (GLUs), enabling an efficient and scalable parallel structure.
Implicationpartial
This is presented as the second key innovation in the abstract and described in the analysis.
Verificationpartial
partial
Evidencepartial
demonstrate that FaST not only delivers superior long-horizon predictive accuracy but also achieves remarkable computational efficiency compared to state-of-the-art baselines.
Implicationpartial
The abstract and analysis both state superior accuracy, and the method evaluation confirms this.
Verificationpartial
partial
Evidencepartial
achieves remarkable computational efficiency compared to state-of-the-art baselines.
Implicationpartial
The abstract and analysis both highlight computational efficiency as a key benefit.
Verificationpartial
partial
Evidencepartial
One limitation is the dependency on having an accurate graph structure, which might not always be available for all real-world scenarios.
Implicationpartial
This is explicitly stated as a limitation in the provided analysis.
Verificationpartial
partial
Evidencepartial
Additionally, while the system shows improvements in computational efficiency, the complexity of setup and maintenance in municipalities with varying technological adoption rates could pose challenges.
Implicationpartial
This is mentioned as a potential challenge in the analysis excerpt.
Verificationpartial
partial
Evidencepartial
The market opportunity is significant given the global push towards smart cities and infrastructure optimization.
Implicationpartial
The 'product_opportunity' section directly addresses the market potential.
Verificationpartial
partial

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FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

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