Abstract: Neurosymbolic AI aims to combine the perceptual capabilities of neural networks with the reasoning capabilities of symbolic systems. Prior works have proposed the use of Answer Set Programming (ASP) as a reasoning engine in neurosymbolic pipelines, but these approaches often depend on classical ASP solvers, creating a scalability bottleneck. Our work focuses on developing differentiable ASP solvers that can be integrated seamlessly with neural networks, enabling scalable and end-to-end trainable neurosymbolic systems. We propose NDProp (Neural Decision-Propagation), a differentiable bottom-up ASP computation method that alternates falsity decisions and truth propagations. Through experiments on standard neurosymbolic benchmarks, NDProp demonstrates strong performance in terms of both accuracy and scalability compared to existing ASP-based neurosymbolic approaches. In future work, we plan to investigate the effectiveness of NDProp in applications that require both large-scale neural perception and complex reasoning. Autonomous driving is our primary target, where safe and efficient deployment requires both real-time perception and complex reasoning.