TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xiachong Lin, Du Yin, Arian Prabowo, Hao Xue, Wen Hu, Imran Razzak, Matthew Amos, Sam Behrens, Flora D. Salim
• Venue: arXiv: cs.AI
• Published: 2026-07-07
• Collected: 2026-07-08
• URL: http://arxiv.org/abs/2607.06349v1
• PDF: https://arxiv.org/pdf/2607.06349v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces TopoBrick, a new framework for forecasting IoT data in buildings without requiring training. It uses building knowledge graphs to create a structural skeleton and employs an agentic topology sampler to select relevant exogenous variables based on the building's physical topology, spatial hierarchy, and operational context.

What the Document Says

• TopoBrick is a framework for zero-shot IoT forecasting in buildings.
• It uses building knowledge graphs to construct a structural skeleton.
• An agentic topology sampler is employed to select target-specific exogenous variables.
• The selected variables are organized based on deployment-time availability.
• Past-known sensor states are separated from future-known calendar, schedule, and meteorological exogenous variables.
• Three real-world buildings were used for testing.
• TopoBrick outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models.
• Ablations show that topology-aware sampling is more reliable than random, ontology-only, or fixed-hop selection.

Key Concepts Mentioned

• TopoBrick
• IoT forecasting
• building knowledge graphs
• agentic topology sampler
• exogenous variables
• physical topology
• spatial hierarchy
• operational context

Claims Made by the Document

• TopoBrick outperforms strong zero-shot foundation-model baselines.
• TopoBrick remains competitive with fully trained building-specific models.

Evidence Used

• Across three real-world buildings, TopoBrick outperformed the baselines.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed covariate sets. We present TopoBrick, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting. TopoBrick uses building knowledge graphs to construct a compact structural skeleton and employs an agentic topology sampler to select target-specific exogenous variables. The selected variables are organized by deployment-time availability, separating past-known sensor states from future-known calendar, schedule, and meteorological exogenous variables. Across three real-world buildings, TopoBrick outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models. Ablations show that topology-aware sampling is more reliable than random, ontology-only, or fixed-hop selection, especially for physically coupled HVAC and weather-driven sensing variables.