MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning
Quick Answer
MultivationBench introduces a benchmark for evaluating multimodal motivation reasoning in AI, revealing that current models struggle with sequential context understanding.
Quick Take
The benchmark integrates psychological frameworks and highlights a gap between static recognition and dynamic reasoning capabilities in models tested.
Key Points
- MultivationBench focuses on story-driven visual narratives for motivation reasoning evaluation.
- Models tested show significant challenges in maintaining consistent motivation across contexts.
- The benchmark is based on Maslow's hierarchy and Reiss's basic desires.
- Results indicate a disconnect between static recognition and dynamic reasoning in AI models.
- The study spans 31 pages with extensive data, including 6 figures and 22 tables.
DeepSignal Analysis
What happened
The authors of MultivationBench introduced a benchmark aimed at assessing multimodal motivation reasoning in AI models. Their findings indicate that current models face challenges in understanding sequential contexts, which is crucial for real-world applications. The benchmark incorporates established psychological theories to evaluate how well models can infer motivations over time.
Key evidence
- MultivationBench is designed to evaluate multimodal motivation reasoning within story-driven visual narratives, addressing a gap in existing evaluations that focus on static contexts.
- The benchmark utilizes psychological frameworks, specifically Maslow's hierarchy and Reiss's basic desires, to guide the evaluation of models.
- Results show that all tested models struggled to maintain consistent motivation reasoning across sequential contexts, highlighting a disconnect between static recognition and dynamic reasoning.
Why it matters
Understanding sequential motivation reasoning is essential for developing AI systems that can interact socially and contextually with humans. The inability of current models to grasp evolving motivations limits their effectiveness in real-world scenarios. By identifying these shortcomings, MultivationBench aims to push the boundaries of AI capabilities in social intelligence and dynamic reasoning.
Paper Resources
📖 Reader Mode
~2 min readAuthors:Kawai Chung, Chunkit Chan, Yauwai Yim, Yuxuan Liu, Haochen Shi, Weiqi Wang, Qing Zong, Tianshi Zheng, Yixuan Fu, Kai Chung Wong, Hao Liang, Yifan Gao, Xi Yang, Janet Hui-wen Hsiao, Yangqiu Song
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.
| Comments: | 31 pages, including appendices; 6 figures and 22 tables. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.26465 [cs.AI] |
| (or arXiv:2607.26465v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.26465 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kawai Chung [view email]
[v1]
Wed, 29 Jul 2026 04:37:59 UTC (5,462 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.