Thinking Ahead: Foresight Intelligence in MLLMs and World Models

arXiv 25.11 2025 31.3 benchmark

TLDR

Introduces Foresight Intelligence and FSU-QA dataset to evaluate VLMs and world models on reasoning about future events.

Reasoning

Strengths include a novel dataset and comprehensive evaluation revealing current model limitations; weaknesses are the narrow VQA format and lack of real-world deployment validation.

Read-first score

Read-first score 31.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 17.

Recency 6%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Methodology quality 18%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark,dataset,experiment

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset

Topical relevance 29%

24.3

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 12%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 116.

Keyword Scores

world model

world dynamics prediction

generative world model

world simulator

interactive world model

video world model

model-based reinforcement learning world model

Deep Analysis

Innovations

Definition of Foresight Intelligence as the capability to anticipate and interpret future events
Introduction of FSU-QA, a new VQA dataset specifically designed to elicit and evaluate Foresight Intelligence
First comprehensive study of state-of-the-art Vision-Language Models (VLMs) under foresight-oriented tasks
Using FSU-QA to assess world models by measuring the semantic coherence of their generated predictions
Demonstration that small VLMs fine-tuned on FSU-QA surpass much larger, advanced models by a substantial margin

Methodology

The authors define Foresight Intelligence and introduce FSU-QA, a VQA dataset designed to elicit and evaluate this capability. They conduct the first comprehensive study of state-of-the-art VLMs on foresight tasks using FSU-QA, and also assess world models by measuring the semantic coherence of their predictions. Additionally, they fine-tune small VLMs on FSU-QA to enhance foresight reasoning.

Key Results

Current VLMs struggle to reason about future situations, but small VLMs fine-tuned on FSU-QA surpass much larger, advanced models by a substantial margin.

Resources

Paper