# AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models

arXiv: https://arxiv.org/abs/2403.13002 · HTML: https://mlsanity.com/paper/2403.13002/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 92 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021)
- [From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design](https://mlsanity.com/paper/2311.12668/) (2023)
- [How Can Large Language Models Help Humans in Design and Manufacturing?](https://mlsanity.com/paper/2307.14377/) (2023)
- [A Survey on Large Language Model Hallucination via a Creativity Perspective](https://mlsanity.com/paper/2402.06647/) (2024)
- [Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design](https://mlsanity.com/paper/2302.02913/) (2023)
- [Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity](https://mlsanity.com/paper/2302.12832/) (2023)
- [Mitigating Uncertainty in Document Classification](https://mlsanity.com/paper/1907.07590/) (2019)
- [Generative Transformers for Design Concept Generation](https://mlsanity.com/paper/2211.03468/) (2022)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers](https://mlsanity.com/paper/2212.13196/) (2022)
- [Conceptual Design Generation Using Large Language Models](https://mlsanity.com/paper/2306.01779/) (2023)
- [Data-Driven Innovation: What Is It](https://mlsanity.com/paper/2201.08184/) (2022)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Combinatorial Optimization of Design Structure Matrix](https://mlsanity.com/paper/2411.12571/) (2024)
- [TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design](https://mlsanity.com/paper/2604.06747/) (2026)
- [A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model](https://mlsanity.com/paper/2407.00396/) (2024)
- [Large Language Models for Combinatorial Optimization of Design Structure Matrix](https://mlsanity.com/paper/2506.09749/) (2025)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)
- [SHARE: Social-Humanities AI for Research and Education](https://mlsanity.com/paper/2604.11152/) (2026)
- [IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs](https://mlsanity.com/paper/2606.00875/) (2026)
- [Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework](https://mlsanity.com/paper/2606.21378/) (2026)
- [TRIZa: Guiding Agent Evolution from Pattern Recognition to Solution Invention](https://mlsanity.com/paper/2610.04555/) (2026)

## Abstract (arXiv)

Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users' knowledge, experience, and reasoning capabilities limit its practicality. To address this, we introduce AutoTRIZ, an artificial ideation system that integrates Large Language Models (LLMs) to automate and enhance the TRIZ methodology. By leveraging LLMs' vast pre-trained knowledge and advanced reasoning capabilities, AutoTRIZ offers a novel, generative, and interpretable approach to engineering innovation. AutoTRIZ takes a problem statement from the user as its initial input, automatically conduct the TRIZ reasoning process and generates a structured solution report. We demonstrate and evaluate the effectiveness of AutoTRIZ through comparative experiments with textbook cases and a real-world application in the design of a Battery Thermal Management System (BTMS). Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, such as SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of AI-driven innovation tools.