Diagnosing Causal Reasoning in Vision-Language Models via Structured Relevance Graphs
arXiv:2602.20878v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning. Existing evaluations primarily assess the correctness of the answers, making it unclear whether failures arise from limited reasoning capability or from misidentifying causally relevant information. We introduce Vision-Language Causal Graphs (VLCGs), a structured, query-conditioned representation that explicitly encodes causally relevant objects, attributes, relations, and scene-grounded assumptions. Building on this representation, we present ViLCaR, a diagnostic benchmark comprising tasks for Causal Attribution, Causal Inference, and Question Answering, along with graph-aligned evaluation metrics that assess relevance identification beyond final answer accuracy. Experiments in state-of-the-art LVLMs show that injecting structured relevance information sign
arXiv:2602.20878v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning. Existing evaluations primarily assess the correctness of the answers, making it unclear whether failures arise from limited reasoning capability or from misidentifying causally relevant information. We introduce Vision-Language Causal Graphs (VLCGs), a structured, query-conditioned representation that explicitly encodes causally relevant objects, attributes, relations, and scene-grounded assumptions. Building on this representation, we present ViLCaR, a diagnostic benchmark comprising tasks for Causal Attribution, Causal Inference, and Question Answering, along with graph-aligned evaluation metrics that assess relevance identification beyond final answer accuracy. Experiments in state-of-the-art LVLMs show that injecting structured relevance information significantly improves attribution and inference consistency compared to zero-shot and standard in-context learning. These findings suggest that current limitations in LVLM causal reasoning stem primarily from insufficient structural guidance rather than a lack of reasoning capacity.
Executive Summary
This article introduces Vision-Language Causal Graphs (VLCGs) to diagnose causal reasoning in Vision-Language Models (VLMs). VLCGs provide a structured representation of causally relevant information, enabling the creation of a diagnostic benchmark called ViLCaR. The results show that state-of-the-art VLMs can significantly improve their causal reasoning capabilities when provided with structured relevance information. This suggests that current limitations in VLMs are due to a lack of structural guidance rather than a lack of reasoning capacity. The study contributes to the development of more robust and reliable VLMs by identifying the importance of causal reasoning and providing a framework for evaluating and improving it.
Key Points
- ▸ Introduction of Vision-Language Causal Graphs (VLCGs) for diagnosing causal reasoning in VLMs
- ▸ Development of the ViLCaR diagnostic benchmark for evaluating VLMs' causal reasoning capabilities
- ▸ Findings indicate that structured relevance information improves VLMs' causal reasoning performance
Merits
Novel Framework
The introduction of VLCGs and ViLCaR provides a novel framework for evaluating and improving VLMs' causal reasoning capabilities.
Improved Performance
The study demonstrates that state-of-the-art VLMs can achieve significant improvements in causal reasoning performance when provided with structured relevance information.
Demerits
Limited Generalizability
The study's findings may not generalize to all types of VLMs or tasks, and further research is needed to explore the applicability of VLCGs and ViLCaR to other domains.
Expert Commentary
The article makes a significant contribution to the field of AI research by introducing a novel framework for evaluating and improving VLMs' causal reasoning capabilities. The use of VLCGs and ViLCaR provides a structured approach to diagnosing causal reasoning in VLMs, which can help to identify areas where these models require improvement. The study's findings have important implications for the development of more robust and reliable AI systems, particularly in areas where causal reasoning is critical. However, further research is needed to explore the generalizability of the study's findings and to develop more comprehensive frameworks for evaluating and improving VLMs' causal reasoning capabilities.
Recommendations
- ✓ Future research should explore the applicability of VLCGs and ViLCaR to other domains and tasks, such as natural language processing or computer vision.
- ✓ The development of more comprehensive frameworks for evaluating and improving VLMs' causal reasoning capabilities should be a priority, including the integration of VLCGs and ViLCaR with other evaluation metrics and methods.