Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A comprehensive evaluation of seventeen Multimodal Reasoning Models (MRMs) across thirteen spatial benchmarks reveals that Chain-of-Thought (CoT) prompting consistently degrades performance in visual spatial reasoning tasks. While CoT has advanced mathematical and logical problem-solving, this study identifies a critical gap in its application to generalized spatial intelligence. Furthermore, a novel No-Image++ ablation demonstrates that both MRMs and CoT-prompted Multimodal Large Language Models (MLMs) exhibit severe shortcut learning, hallucinating visual details based on textual priors even when no image is present. These findings challenge the effectiveness of text-only CoT for spatial tasks and highlight the necessity for developing vision-centric reasoning paradigms.

Key takeaway

For AI Engineers developing multimodal reasoning systems, you should re-evaluate the application of Chain-of-Thought prompting for visual spatial tasks. Your current CoT implementations may be degrading performance and introducing shortcut learning, leading to hallucinated visual details. Consider integrating vision-centric reasoning paradigms to improve spatial intelligence and reduce reliance on textual priors.

Key insights

Chain-of-Thought prompting degrades visual spatial reasoning in MRMs and leads to shortcut learning.

Principles

• Text-only CoT is ineffective for spatial tasks.
• MRMs can hallucinate visual details from text.

Method

Evaluated 17 models on 13 spatial benchmarks and used a No-Image++ ablation to detect shortcut learning and visual hallucination from textual priors.

In practice

• Avoid text-only CoT for visual spatial tasks.
• Prioritize vision-centric reasoning paradigms.

Topics

• Multimodal Reasoning Models
• Chain-of-Thought
• Visual Spatial Reasoning
• Shortcut Learning
• Model Hallucination

Best for: AI Engineer, Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.