From Prompts to Tokens: Internalizing Causal Supervision in Vision-Language Model for Multi-Image Causal Reasoning
arXiv:2606.11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual inputs and reasoning over intervention effects. Despite recent progress, large vision--language models (VLMs) remain brittle at such tasks, especially for interventional and counterfactual queries over multi-image inputs. Most existing explorations inject causal knowledge via textual prompts, leaving causal mechanisms external to model execution and limiting reliable control during inference. To
The continuous evolution of large language models and vision-language models necessitates addressing their limitations in complex reasoning tasks, pushing researchers to explore more robust causal mechanisms.
Improving causal reasoning in VLMs is crucial for developing AI systems that can reliably understand, predict, and intervene in real-world scenarios, moving beyond superficial pattern recognition.
This research suggests a shift from external prompt-based causal knowledge injection to internalizing causal mechanisms within VLMs, leading to more reliable and controllable AI inference.
- · AI developers
- · Robotics
- · Autonomous systems
- · Healthcare diagnostics
- · AI systems brittle at causal reasoning
- · Prompt engineering alone for complex AI tasks
More sophisticated and reliable AI models capable of complex visual causal reasoning will emerge.
This enhanced capability will accelerate the deployment of autonomous systems in high-stakes environments, such as medical interventions and advanced manufacturing.
Improved AI understanding of causality could lead to breakthroughs in scientific discovery by enabling systems to identify and test causal hypotheses from observational data.
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Read at arXiv cs.AI