INTCORT: Training-Free Spatial Reasoning Enhancement for Vision-Language Models via Input Transformations and Confidence Routing

arXiv:2609.24813 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Benchmark — 26× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Aggregation — 6× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Catastrophic Forgetting — 3× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Hallucination — 2× in this paper · explained in 3 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• HIVTP: A Training-Free Method to Improve VLMs Efficiency via Hierarchical Visual Token Pruning Using Middle-Layer-Based Importance Score
• Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
• Investigating the Catastrophic Forgetting in Multimodal Large Language Models
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Qwen3-VL Technical Report

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