Left-Right Symmetry Breaking in CLIP-style Vision-Language Models Trained on Synthetic Spatial-Relation Data
arXiv:2601.12809v2 Announce Type: replace-cross Abstract: Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired, and if so, through what mechanisms. We present a controllable 1D image-text testbed to probe how left-right relational understanding emerges in Transformer-based vision and text encoders trained with a CLIP-style contrastive objective. We train lightweight Transformer-based vision and text encoders end-to-end on paired descriptions of one- and two-object scenes and evaluate generalization to unse
The continuous development and scaling of vision-language models necessitate deeper understanding of their fundamental capabilities and limitations, particularly in spatial reasoning.
Improving spatial understanding in AI is crucial for real-world applications in robotics, autonomous systems, and advanced AI agents, pushing beyond statistical correlations to true comprehension.
This research provides a controlled testbed and methodology to rigorously probe and enhance a core deficiency in current vision-language models, influencing future architectural and training paradigms.
- · AI researchers
- · Robotics companies
- · Autonomous vehicle developers
- · AI ethics and safety organizations
- · Developers relying solely on superficial VLM capabilities
- · Companies with high-stakes spatial reasoning AI applications without robust unde
More robust and reliable vision-language models will emerge with enhanced spatial reasoning.
This foundational improvement will accelerate the development and deployment of advanced AI agents capable of navigating and interacting with complex physical environments.
Improved spatial understanding could lead to new forms of human-robot collaboration and more intuitive AI interactions in augmented and virtual realities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG