[2605.02910] CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
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Computer Science > Artificial Intelligence
arXiv:2605.02910 (cs)
[Submitted on 6 Apr 2026 (v1), last revised 6 May 2026 (this version, v2)]
Title:CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
Authors:Cheng Qian, Hyeonjeong Ha, Jiayu Liu, Jeonghwan Kim, Jiateng Liu, Bingxuan Li, Aditi Tiwari, Dwip Dalal, Zhenhailong Wang, Xiusi Chen, Mahdi Namazifar, Yunzhu Li, Heng Ji
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Abstract:Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains underexplored. We study this capability through the lens of creative tool use, where a model repurposes available objects by reasoning about their affordances and attributes rather than relying on canonical usage. As a first step, we introduce CreativityBench, a benchmark for evaluating affordance-based creativity in LLMs. To this end, we build a large-scale affordance knowledge base (KB) with 4K entities and 150K+ affordance annotations, explicitly linking objects, parts, attributes, and actionable uses. Building on this KB, we generate 14K grounded tasks that require identifying non-obvious yet physically plausible solutions under constraints. Evaluations across 10 state-of-the-art LLMs, including closed and open-source models, show that models can often select a plausible object, but fail to identify the correct parts, their affordances, and the underlying physical mechanism needed to solve the task, leading to a significant drop in performance. Furthermore, improvements from model scaling quickly saturate, strong general reasoning does not reliably translate to creative affordance discovery, and common inference-time strategies such as Chain-of-Thought yield limited gains. These results suggest that creative tool use remains a major challenge for current models, and that CreativityBench provides a useful testbed for studying this missing dimension of intelligence, with potential implications for planning and reasoning modules in future agents.
Comments: 57 Pages, 14 Tables, 27 Figures
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.02910 [cs.AI]
(or arXiv:2605.02910v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.02910
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arXiv-issued DOI via DataCite
Submission history
From: Cheng Qian [view email] [v1] Mon, 6 Apr 2026 16:33:23 UTC (32,366 KB)
[v2] Wed, 6 May 2026 05:10:39 UTC (32,368 KB)
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