Inference Labs' Verifiable AI Revolution Deterministically, despite the current power of AI, the lack of definitive proof means it remains a "black box" that cannot be fully trusted, which is unacceptable to the general public. In high-risk fields such as robotics and finance, flawed decisions can lead to catastrophic consequences. Therefore, these industries urgently need verifiable systems to ensure the authenticity of AI outputs. ■ The "Verifiable Architecture" Built with DSperse and JSTprove Inference Labs (@inference_labs) has proposed an innovative system architecture to address this pain point, achieving a leap from zkML theory to production-scale deployment, laying a solid foundation for AI verification: ➡️DSperse (Distributed Slicing Technology): This framework identifies and slices the model into multiple parallel computational slices, greatly reducing node load and computational latency. ➡️JSTprove (Lightweight Proof System): By hiding complex cryptographic details through a simplified CLI interface, JSTprove achieves a 65% improvement in proof speed and successfully reduces memory usage to below 1GB. ➡️Data Verification: As of... By the end of 2025, the network had processed over 281 million zkML proofs, demonstrating its stability and efficiency in handling real-world workloads. Through a modular and open-source strategy, Inference Labs (@inference_labs) is building a decentralized AI proof network to prevent AI technology from being monopolized by a few centralized giants, ensuring the democratization and transparency of AI development.