Thinking Ahead: Foresight Intelligence in MLLMs and World Models
This addresses the need for better foresight capabilities in applications like autonomous driving, though it is incremental as it focuses on dataset creation and benchmarking.
The authors tackled the problem of evaluating foresight intelligence in vision-language models by introducing FSU-QA, a new VQA dataset, and found that current models struggle with future reasoning, but fine-tuning small models on FSU-QA enabled them to surpass larger advanced models by a substantial margin.
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 still struggle to reason about future situations. Beyond serving as a benchmark, FSU-QA also enables the assessment of world models by measuring the semantic coherence of their generated predictions, quantified through performance gains when VLMs are augmented with such outputs. Our experiments further demonstrate that FSU-QA can effectively enhance foresight reasoning: even small VLMs fine-tuned on FSU-QA surpass much larger, advanced models by a substantial margin. Together, these findings position FSU-QA as a principled foundation for developing next-generation models capable of truly anticipating and understanding future events.