# CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models

arXiv: https://arxiv.org/abs/2502.12066 · HTML: https://mlsanity.com/paper/2502.12066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study](https://mlsanity.com/paper/2404.10719/) (2024)
- [Seven Failure Points When Engineering a Retrieval Augmented Generation System](https://mlsanity.com/paper/2401.05856/) (2024)
- [LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs](https://mlsanity.com/paper/2406.15319/) (2024)
- [Reinforcement Learning for LLM Post-Training: A Survey](https://mlsanity.com/paper/2407.16216/) (2024)
- [Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective](https://mlsanity.com/paper/2404.04626/) (2024)
- [Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization](https://mlsanity.com/paper/2402.14182/) (2024)
- [Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting](https://mlsanity.com/paper/2108.06764/) (2021)
- [EyeTrans: Merging Human and Machine Attention for Neural Code Summarization](https://mlsanity.com/paper/2402.14096/) (2024)
- [Modeling Programmer Attention as Scanpath Prediction](https://mlsanity.com/paper/2308.13920/) (2023)
- [Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem](https://mlsanity.com/paper/2401.14876/) (2024)
- [Leveraging Artificial Intelligence on Binary Code Comprehension](https://mlsanity.com/paper/2210.05103/) (2022)

## Built on by (most cited first, 1 of 1)

- [BuildArena: A Physics-Aligned Interactive Benchmark of LLMs for Engineering Construction](https://mlsanity.com/paper/2510.16559/) (2025)

## Abstract (arXiv)

Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored. In commercial construction, the complexity of automated scheduling often requires manual intervention to ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to optimize construction schedules in complex projects like semiconductor fabrication. CONSTRUCTA addresses key challenges by: (1) integrating construction-specific knowledge through static RAG; (2) employing context-sampling techniques inspired by architectural expertise to provide relevant input; and (3) deploying Construction DPO to align schedules with expert preferences using RLHF. Experiments on proprietary data demonstrate performance improvements of +42.3% in missing value prediction, +79.1% in dependency analysis, and +28.9% in automated planning compared to baseline methods, showcasing its potential to revolutionize construction workflows and inspire domain-specific LLM advancements.