arXiv:2511.18735v3 Announce Type: replace-cross Abstract: In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applications such as autonomous driving, yet largely overlooked by existing research. To bridge this gap, we introduce FSU-QA, a new Visual Question-Answering (VQA) dataset specifically designed to elicit and evaluate Foresight Intelligence. Using FSU-QA, we conduct the first comprehensive study of state-of-the-art Vision-Language Models (VLMs) under foresight-oriented tasks, revealing that current models s
The proliferation of advanced AI models across complex, real-world applications highlights the urgent need for robust foresight capabilities beyond current VLM limitations.
Achieving 'Foresight Intelligence' in AI systems is critical for safety, reliability, and unlocking autonomous applications in high-stakes environments like autonomous driving.
This work introduces a new benchmark and conceptual framework, potentially shifting research focus towards anticipatory reasoning in multimodal AI, rather than just reactive capabilities.
- · AI Safety Researchers
- · Autonomous Vehicle Developers
- · Multimodal AI Developers
- · Robotics
- · AI applications lacking robust foresight
- · Traditional VLM benchmarks as sole evaluation metrics
Current state-of-the-art VLMs are found lacking in foresight intelligence, revealing a significant gap in their capabilities for real-world application.
Increased research and development will focus on building AI architectures specifically designed for anticipating and interpreting future events, leading to more robust autonomous systems.
The definition and evaluation of 'intelligence' in AI may expand to include foresight as a core metric, influencing future AI development paradigms and regulatory frameworks.
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Read at arXiv cs.AI