VIVA+: Human-Centered Situational Decision-Making

• URL: http://arxiv.org/abs/2509.23698v1
• Date: Sun, 28 Sep 2025 07:13:11 GMT
• Title: VIVA+: Human-Centered Situational Decision-Making
• Authors: Zhe Hu, Yixiao Ren, Guanzhong Liu, Jing Li, Yu Yin,
• Abstract summary: We introduce VIVA+, a benchmark for evaluating the reasoning and decision-making of MLLMs in human-centered situations.<n>Vila+ consists of 1,317 real-world situations paired with 6,373 multiple-choice questions, targeting three core abilities for decision-making.<n>We evaluate the latest commercial and open-source models on VIVA+, where we reveal distinct performance patterns and highlight significant challenges.
• Score: 9.67738226553979
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) show promising results for embodied agents in operating meaningfully in complex, human-centered environments. Yet, evaluating their capacity for nuanced, human-like reasoning and decision-making remains challenging. In this work, we introduce VIVA+, a cognitively grounded benchmark for evaluating the reasoning and decision-making of MLLMs in human-centered situations. VIVA+ consists of 1,317 real-world situations paired with 6,373 multiple-choice questions, targeting three core abilities for decision-making: (1) Foundational Situation Comprehension, (2) Context-Driven Action Justification, and (3) Reflective Reasoning. Together, these dimensions provide a systematic framework for assessing a model's ability to perceive, reason, and act in socially meaningful ways. We evaluate the latest commercial and open-source models on VIVA+, where we reveal distinct performance patterns and highlight significant challenges. We further explore targeted training and multi-step reasoning strategies, which yield consistent performance improvements. Finally, our in-depth analysis highlights current model limitations and provides actionable insights for advancing MLLMs toward more robust, context-aware, and socially adept decision-making in real-world settings.

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