Year: 2026
Type: conference-paper
Abstract: Large language models are increasingly used in inventive problem-solving, but effective support requires more than open-ended idea generation.Inventive problem-solving requires improving one aspect of a technical system without unintentionally worsening another.TRIZ (Theory of Inventive Problem Solving) provides a unique and structured framework for this setting by representing engineering tradeoffs as contradictions and linking them to standardized inventive principles.However, prior TRIZ-LLM evaluations are typically smallscale, case studies in focused areas of technology, and rarely grounded in patent text, which makes it difficult to assess structured reasoning at scale.We introduce TRIZBENCH, a dataset and benchmark for TRIZ reasoning grounded in open technical sources and U.S. patents.TRIZBENCH evaluates the core TRIZ workflow through three tasks: contradiction prediction, inventive principle prediction, and grounded TRIZ reasoning.Experiments with multiple LLM baselines show that detecting contradictions is easier than recovering correct trade-off pairs, while principle prediction benefits from explicitly exploiting TRIZ structure.Our findings further underscore the importance of grounding.We show that semantic retrieval enables evidence-based justifications and helps explain why LLMs fail.
Authors: Zhu Wang, Brian Uzzi
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Funder: Northwestern University
OpenAlex ID: W7166836694