TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
TRIZ, the Theory of Inventive Problem Solving, is a structured, knowledge-based framework for innovation and abstracting problems to find inventive solutions. However, its application is often limited by the complexity and deep interdisciplinary knowledge required. Advancements in Large Language Models (LLMs) have revealed new possibilities for automating parts of this process. While previous studies have explored single LLMs in TRIZ applications, this paper introduces a multi-agent approach. We propose an LLM-based multi-agent system, called TRIZ agents, each with specialized capabilities and tool access, collaboratively solving inventive problems based on the TRIZ methodology. This multi-agent system leverages agents with various domain expertise to efficiently navigate TRIZ steps. The aim is to model and simulate an inventive process with language agents. We assess the effectiveness of this team of agents in addressing complex innovation challenges based on a selected case study in engineering. We demonstrate the potential of agent collaboration to produce diverse, inventive solutions. This research contributes to the future of AI-driven innovation, showcasing the advantages of decentralized problem-solving in complex ideation tasks.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 17 of its 49 references that are in the map. First posted on arXiv 23 Jun 2025.
Builds on
17 of 49 references.
Closest ancestors · 3
Same direction, newest first
- 2024Automated Design of Agentic Systems15 Aug
- 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects7 Jan
- 2023Cognitive Architectures for Language Agents5 Sep
Other references · 14
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- 2024Large Language Models: A Surveycited by 525
- 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planningcited by 231
- 2023An Empirical Study of the Non-determinism of ChatGPT in Code Generationcited by 137
- 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 93
- 2024LLMs Will Always Hallucinate, and We Need to Live With Thiscited by 78
- 2022The Creativity of Text-to-Image Generationcited by 73
- 2024ChatMusician: Understanding and Generating Music Intrinsically with LLMcited by 68
- 2024Improving Large Models with Small models: Lower Costs and Better Performancecited by 14
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
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Close in wording, no citation either way
- 2024AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models
- 2025Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
- 2024Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs
- 2026EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
- 2026Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- 2024MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming