MK2 at PBIG Competition: A Prompt Generation Solution

The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic leaderboard and won 25 of 36 tests. Only the materials-chemistry track lagged, indicating the need for deeper domain grounding; yet, the results show that lightweight prompt engineering has already delivered competitive, commercially relevant ideation from patents.

Publication Details

Journal
ArXiv.org
Published
2025-07-11
Primary Topic
Machine Learning in Materials Science
Type
article
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article

MK2 at PBIG Competition: A Prompt Generation Solution

ArXiv.org
Machine Learning in Materials Science
article

MK2 at PBIG Competition: A Prompt Generation Solution

article en

Abstract

The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic leaderboard and won 25 of 36 tests. Only the materials-chemistry track lagged, indicating the need for deeper domain grounding; yet, the results show that lightweight prompt engineering has already delivered competitive, commercially relevant ideation from patents.

ArXiv.org
Industry, innovation and infrastructure
Openalex Percentile: Top 60%
Machine Learning in Materials Science
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MK2 at PBIG Competition: A Prompt Generation Solution · ArXiv.org (2025) | TGRS Research Map | TGRS