When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning
arXiv:2602.21619v1 Announce Type: new 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, excessi
arXiv:2602.21619v1 Announce Type: new 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.
Executive Summary
This paper conducts a systematic analysis of information injection for visual spatial reasoning (VSR) in modern vision-language models (VLMs). The authors examine the impact of spatial contexts, commonsense knowledge, and chain-of-thought (CoT) prompting on VSR performance. The results reveal that more information does not necessarily yield better reasoning, highlighting the importance of selective, task-aligned information injection. The findings provide practical guidance for designing reliable multimodal reasoning pipelines. The study's approach and conclusions contribute to the ongoing effort to improve VLMs' ability to perform VSR tasks.
Key Points
- ▸ Targeted single spatial cues outperform multi-context aggregation in VSR tasks.
- ▸ Excessive or weakly relevant commonsense knowledge degrades VSR performance.
- ▸ CoT prompting improves accuracy only when spatial grounding is sufficiently precise.
Merits
Strength
The study's systematic and hypothesis-driven approach provides a rigorous analysis of information injection in VSR tasks.
Strength
The findings offer practical guidance for designing reliable multimodal reasoning pipelines, which can improve the performance of VLMs in VSR tasks.
Demerits
Limitation
The study focuses on three representative VLMs and two public benchmarks, which may limit the generalizability of the findings to other VLMs and benchmarks.
Limitation
The analysis does not explore the impact of other factors, such as data quality and model architecture, on VSR performance.
Expert Commentary
The study's findings have significant implications for the development of VLMs and their applications in VSR tasks. The results highlight the importance of selective, task-aligned information injection, which can improve the performance of VLMs in VSR tasks. The study's approach and conclusions contribute to the ongoing effort to improve VLMs' ability to perform multimodal reasoning tasks, including VSR. The findings also suggest that policymakers should prioritize the development of reliable multimodal reasoning pipelines to improve the performance of VLMs in VSR tasks. Overall, the study provides a rigorous analysis of information injection in VSR tasks and offers practical guidance for designing reliable multimodal reasoning pipelines.
Recommendations
- ✓ Future studies should explore the impact of other factors, such as data quality and model architecture, on VSR performance.
- ✓ Developers should prioritize the design of task-aligned information injection mechanisms to improve VSR performance.
