MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning
arXiv:2607.26465
The paper introduces MultivationBench, a benchmark that tests multimodal large language models on their ability to reason about evolving human motivations across sequential visual story contexts.
Abstract
Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MultivationBench, a benchmark designed to rigorously evaluate multimodal motivation reasoning within story-driven visual narratives. The benchmark builds upon established psychological frameworks - Maslow's hierarchy and Reiss's basic desires - and requires models to integrate accumulated multimodal context to infer evolving motivations. Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.
31 pages, including appendices; 6 figures and 22 tables. Code: https://github.com/HKUST-KnowComp/MultivationBench