When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning

2026-02-25

AI Summary

This study investigates the impact of external information—such as spatial cues, commonsense knowledge, and chain-of-thought prompts—on visual spatial reasoning (VSR) performance. Through hypothesis-driven controlled experiments on two public benchmarks, the authors systematically evaluate three categories of vision-language models. Their findings reveal that a single, precise spatial cue consistently outperforms multi-context fusion; weakly relevant or excessive commonsense knowledge degrades performance; and chain-of-thought prompting is beneficial only when spatial localization is sufficiently accurate. This work is the first to demonstrate that “more information is not always better” in VSR, advocating instead for the selective injection of task-aligned signals and clarifying the conditions under which spatial localization and chain-of-thought reasoning synergistically enhance performance.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Visual spatial reasoning (VSR) remains challenging for modern vision-language models (VLMs), despite advances in multimodal architectures. A common strategy is to inject additional information at inference time, such as explicit spatial cues, external commonsense knowledge, or chain-of-thought (CoT) reasoning instructions. However, it remains unclear when such information genuinely improves reasoning and when it introduces noise. In this paper, we conduct a hypothesis-driven analysis of information injection for VSR across three representative VLMs and two public benchmarks. We examine (i) the type and number of spatial contexts, (ii) the amount and relevance of injected commonsense knowledge, and (iii) the interaction between spatial grounding and CoT prompting. Our results reveal a consistent pattern: more information does not necessarily yield better reasoning. Targeted single spatial cues outperform multi-context aggregation, excessive or weakly relevant commonsense knowledge degrades performance, and CoT prompting improves accuracy only when spatial grounding is sufficiently precise. These findings highlight the importance of selective, task-aligned information injection and provide practical guidance for designing reliable multimodal reasoning pipelines.

Problem

• visual spatial reasoning
• information injection
• vision-language models
• commonsense knowledge
• spatial grounding

Innovation

• visual spatial reasoning
• information injection
• vision-language models
• commonsense knowledge
• chain-of-thought prompting