Paper
An Interactive Multi-Agent System for Evaluation of New Product Concepts
Authors
Bin Xuan, Ruo Ai, Hakyeon Lee
Abstract
Product concept evaluation is a critical stage that determines strategic resource allocation and project success in enterprises. However, traditional expert-led approaches face limitations such as subjective bias and high time and cost requirements. To support this process, this study proposes an automated approach utilizing a large language model (LLM)-based multi-agent system (MAS). Through a systematic analysis of previous research on product development and team collaboration, this study established two primary evaluation dimensions, namely technical feasibility and market feasibility. The proposed system consists of a team of eight virtual agents representing specialized domains such as R&D and marketing. These agents use retrieval-augmented generation (RAG) and real-time search tools to gather objective evidence and validate concepts through structured deliberations based on the established criteria. The agents were further fine-tuned using professional product review data to enhance their judgment accuracy. A case study involving professional display monitor concepts demonstrated that the system's evaluation rankings were consistent with those of senior industry experts. These results confirm the usability of the proposed multi-agent-based evaluation approach for supporting product development decisions.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.05980v1</id>\n <title>An Interactive Multi-Agent System for Evaluation of New Product Concepts</title>\n <updated>2026-03-06T07:22:52Z</updated>\n <link href='https://arxiv.org/abs/2603.05980v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.05980v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Product concept evaluation is a critical stage that determines strategic resource allocation and project success in enterprises. However, traditional expert-led approaches face limitations such as subjective bias and high time and cost requirements. To support this process, this study proposes an automated approach utilizing a large language model (LLM)-based multi-agent system (MAS). Through a systematic analysis of previous research on product development and team collaboration, this study established two primary evaluation dimensions, namely technical feasibility and market feasibility. The proposed system consists of a team of eight virtual agents representing specialized domains such as R&D and marketing. These agents use retrieval-augmented generation (RAG) and real-time search tools to gather objective evidence and validate concepts through structured deliberations based on the established criteria. The agents were further fine-tuned using professional product review data to enhance their judgment accuracy. A case study involving professional display monitor concepts demonstrated that the system's evaluation rankings were consistent with those of senior industry experts. These results confirm the usability of the proposed multi-agent-based evaluation approach for supporting product development decisions.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-06T07:22:52Z</published>\n <arxiv:comment>46 pages, 3 figures + This paper proposes an LLM-based multi-agent system (MAS) for automated evaluation of new product concepts, incorporating retrieval-augmented generation (RAG) and cross-functional virtual agents to assess technical and market feasibility</arxiv:comment>\n <arxiv:primary_category term='cs.AI'/>\n <author>\n <name>Bin Xuan</name>\n </author>\n <author>\n <name>Ruo Ai</name>\n </author>\n <author>\n <name>Hakyeon Lee</name>\n </author>\n </entry>"
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