🤖AI Summary
Researchers investigated whether Vision-Language Models (VLMs) can reason robustly under distribution shifts and found that fine-tuned VLMs achieve high accuracy in-distribution but fail to generalize. They propose VLC, a neuro-symbolic method combining VLM-based concept recognition with circuit-based symbolic reasoning that demonstrates consistent performance under covariate shifts.
Key Takeaways
- →Fine-tuned VLMs achieve high in-distribution accuracy but fail to generalize under covariate shifts in visual reasoning tasks.
- →Traditional gradient-based end-to-end training does not reliably induce underlying reasoning functions in VLMs.
- →Recent neuro-symbolic approaches with black-box reasoning components still exhibit inconsistent robustness across tasks.
- →The proposed VLC method decouples perception from reasoning by combining VLM concept recognition with circuit-based symbolic execution.
- →VLC consistently achieves strong performance under covariate shifts across three distinct visual deductive reasoning tasks.
#vision-language-models#vlm#neuro-symbolic#reasoning#robustness#distribution-shifts#symbolic-reasoning#machine-learning#ai-research
Read Original →via arXiv – CS AI
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