Adaptive Visual Imagination Control: A Test-Time Scaling Strategy for World Model-Based Visual Spatial Reasoning

Original Author/Maintainer: Yui010206 Source Platform: GitHub Publication Date: June 2, 2026 Core Idea: This study focuses on the key problem of "when to imagine and how much to imagine" in visual spatial reasoning, proposing an adaptive test-time scaling method that uses world models to enhance AI's visual spatial reasoning capabilities and achieve an optimal balance between performance and computational efficiency. Keywords: Visual Reasoning, World Model, Test-Time Scaling, Adaptive Control, Spatial Reasoning, World Model, Test-Time Scaling, AI

Visual spatial reasoning is one of the core capabilities of human intelligence, but AI systems face many challenges:

• Limitations of Traditional Methods: Pure perception lacks dynamic modeling capabilities, explicit reasoning struggles with complex scenarios, end-to-end learning lacks interpretability and requires large amounts of data.
• Rise of World Models: In recent years, it has become a new direction for solving visual reasoning, capable of constructing dynamic representations of the environment, predicting future states, and performing imaginative planning, but it has not addressed the core problem of "when to imagine and how much to imagine".

Core Problem: Traditional fixed test-time computational budgets have flaws of resource waste (for simple tasks) or insufficient capability (for complex tasks), requiring adaptive adjustment of computational investment. Research Contributions: Proposes an adaptive imagination control framework, whose core is to enable the model to learn to judge when to imagine and the degree of imagination:

• Framework Components: World model (internally simulates scene changes), policy network (decides when to stop imagining), value estimation (evaluates the value of imagination).
• Key Innovations: Dynamic imagination depth, early termination mechanism, imagination quality assessment.

World Model Architecture: Based on Transformer, realizing state representation, dynamic prediction, multi-step deduction, and uncertainty modeling. Adaptive Control Strategy: Trained with reinforcement learning, aiming to maximize accuracy, minimize computational cost, and balance exploration and exploitation. Test Tasks: Path planning, object tracking, spatial relationship reasoning, physical simulation.

• Performance Comparison: Accuracy increased by 15-25% under the same budget; computational volume reduced by 30-50% at the same accuracy; robustness enhanced.
• Adaptive Behavior: Simple tasks use 1-2 steps of imagination, complex tasks use 5-10 steps; 40% of tasks terminate early; uncertainty guides more imagination.
• Ablation Experiments: Removing the world model/adaptive strategy/value estimation all lead to performance degradation, proving the importance of each component.

Technical Significance:

• Visual Reasoning: From passive perception to active imagination, from fixed processes to adaptive decision-making.
• Test-Time Scaling: Provides an adaptive paradigm, extended to the visual domain, optimizing the efficiency-performance trade-off.
• World Model: Realizes the combination of imagination control and decision-making. Application Prospects: Robot navigation, autonomous driving, augmented reality, game AI, etc.

Current Limitations: The quality of the world model affects performance; high training cost; generalization ability needs improvement; insufficient interpretability. Future Directions: More powerful world models; meta-learning to adapt to new tasks; human-machine collaboration; multi-modal expansion; theoretical analysis of optimality.

The adaptive visual imagination control framework proposed in this study achieves a balance between performance and efficiency by dynamically adjusting the depth of imagination, demonstrating a new paradigm of AI reasoning from fixed processes to adaptive decision-making and from passive perception to active imagination, which is expected to play an important role in multiple fields.