# Generative manufacturing systems using diffusion models and ChatGPT

arXiv: https://arxiv.org/abs/2405.00958 · HTML: https://mlsanity.com/paper/2405.00958/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 2 May 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 1 of 1)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)

## Built on by (most cited first, 3 of 3)

- [CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion](https://mlsanity.com/paper/2509.13688/) (2025)
- [Mirage: Unveiling Hidden Artifacts in Synthetic Images with Large Vision-Language Models](https://mlsanity.com/paper/2510.03840/) (2025)
- [Text2Arch: A Dataset for Generating Scientific Architecture Diagrams from Natural Language Descriptions](https://mlsanity.com/paper/2604.14941/) (2026)

## Abstract (arXiv)

In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.