mlsanityData to 7 Oct 2026

GRID: Graph-based Reasoning for Intervention and Discovery in Built Environments

arXiv 2509.16397 ↗First posted 19 Sep 2025cs.LG cs.AIcited by 0

On the map: LLM reasoning → Causal reasoning in LLMs

Abstract (arXiv)

Manual HVAC fault diagnosis in commercial buildings takes 8-12 hours per incident and achieves only 60 percent diagnostic accuracy, reflecting analytics that stop at correlation instead of causation. To close this gap, we present GRID (Graph-based Reasoning for Intervention and Discovery), a three-stage causal discovery pipeline that combines constraint-based search, neural structural equation modeling, and language model priors to recover directed acyclic graphs from building sensor data. Across six benchmarks: synthetic rooms, EnergyPlus simulation, the ASHRAE Great Energy Predictor III dataset, and a live office testbed, GRID achieves F1 scores ranging from 0.65 to 1.00, with exact recovery (F1 = 1.00) in three controlled environments (Base, Hidden, Physical) and strong performance on real-world data (F1 = 0.89 on ASHRAE, 0.86 in noisy conditions). The method outperforms ten baseline approaches across all evaluation scenarios. Intervention scheduling achieves low operational impact in most scenarios (cost <= 0.026) while reducing risk metrics compared to baseline approaches. The framework integrates constraint-based methods, neural architectures, and domain-specific language model prompts to address the observational-causal gap in building analytics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning. Builds on 9 of its 63 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

9 of 63 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024CausalEval: Towards Better Causal Reasoning in Language Models22 Oct
  2. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Framework2 May
  3. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causality28 Apr

Other references · 6

  1. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
  2. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 146
  3. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  4. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  5. 2023ICCPS: Impact discovery using causal inference for cyber attacks in CPSscited by 4
  6. 2024Bridging the Gap Between Data-Driven And Theory-Driven Modelling - Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systemscited by 1

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  4. 2024Fault Diagnosis in Power Grids with Large Language Model
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