This is the first production-ready zero-knowledge machine learning (zkML) system capable of successfully generating cryptographic proofs for complete large language model (LLM) inference. This release signifies that verifiable artificial intelligence is no longer an unattainable vision but has become a reality.
The launch of DeepProve-1 means we have successfully generated zero-knowledge proofs for OpenAI's GPT-2, marking a significant breakthrough in the field of verifiable artificial intelligence. This is not only a technical achievement but also lays a solid foundation for zkML support for the next generation of large language models (such as LLAMA, Gemma, etc.). Given the architectural similarities between GPT-2 and Meta's LLAMA, DeepProve aims to bridge this final gap as quickly as possible, supporting the most widely adopted open-source LLMs globally.
The launch of DeepProve-1 marks a turning point in the development of machine learning, making verifiability a core feature of modern AI systems. As AI increasingly becomes a crucial driving force in decision-making across sectors like defense, healthcare, finance, and infrastructure, DeepProve-1 brings cryptographic integrity assurance to these critical systems.
Effort required to prove GPT-2
The process of proving GPT-2 reasoning involves extensive work in cryptography, systems engineering, and machine learning. Since our last major milestone, Lagrange's research and engineering team has dedicated efforts to support the structural and computational patterns that define transformer architectures. To this end, we have significantly expanded the capabilities of the DeepProve framework, introducing support for complex graph structures to accommodate the architectures of real models.
Innovations of DeepProve-1 include:
Support for arbitrary graph structures: Modern LLMs are typically non-linear, utilizing computational graphs. We have added support for complex graphs to DeepProve, enabling it to handle non-linear inputs with residual connections and parallel branches.
Introduction of general and transformer-specific layers: DeepProve has added multiple layers to support the proof of GPT-2. These layers include additive layers, ConcatMatMul, Softmax, etc., all optimized to ensure the highest proof accuracy.
GGUF format support: We have added support for the GGUF format, allowing DeepProve to efficiently handle widely used LLMs, greatly enhancing the model's compatibility and operability.
Dedicated LLM inference engine: Unlike traditional neural networks, LLMs are autoregressive. DeepProve-1 introduces a dedicated module to manage state and incrementally generate proofs.
Performance optimization and future outlook
Although DeepProve-1 is already capable of proving real LLM reasoning, our next focus is on performance optimization. To achieve the practicality of zkML in production environments, especially in high-throughput or real-time scenarios, performance must match accuracy. We are optimizing DeepProve to enhance its cryptographic efficiency and system-level parallelism.
DeepProve-1 is not only a technological milestone but also a proof of possibilities. For the first time, the feasibility of zero-knowledge proof for LLM reasoning opens the door to the deployment of verifiable AI. Its wide application potential in defense, healthcare, and finance signifies that more AI models will be able to achieve auditability and transparency in the future.
The success of DeepProve-1 proves the reasoning of GPT-2, marking a new milestone in the evolution of machine learning. Next, we will continue to optimize the performance of DeepProve, especially the scaling to large models like LLAMA. With the profound impact of AI technology on our lives, ensuring the correctness of decisions is no longer a theoretical advantage but a necessity for humanity.
Lagrange Labs is leading the future, making the behavior of every AI model auditable and every inference provable. The era of secure and verifiable AI has arrived, and DeepProve will continue to illuminate the path ahead.@Lagrange Official #lagrange $LA
