@Lagrange Official Announced that its DeepProve-1 successfully generated a zero-knowledge proof for the complete GPT-2 model inference, which has caused a stir in the AI and cryptography communities. This is not only a global first milestone, but more importantly, the huge technical challenges that have been overcome behind it. To understand the significance of this achievement, we must delve into the technical details and understand which 'mountains' Lagrange has conquered that hinder zkML from becoming practical.
The first mountain:
Non-linear computational graph structure. Traditional simple ML models (like MLP) are linear sequences of layers, while modern LLMs are complex computational graphs that include residual connections and parallel branches. To support this, DeepProve must reconstruct from a linear proof framework to a graph-native system that can understand and prove any computational path, marking a fundamental leap in architecture.
The second mountain:
Cryptographic implementation of the core layer of the Transformer. The core of LLM lies in its Transformer architecture, which contains multiple layers that are extremely unfriendly to ZK proofs. For example, the Softmax layer is very difficult to prove due to its sensitivity to floating-point precision;
Matrix multiplication and concatenation operations in the multi-head attention mechanism require handling higher-dimensional data. Lagrange's R&D team not only implemented ZK versions of these layers but also optimized performance while ensuring cryptographic security.
The third mountain:
Compatibility of mainstream model formats. If a zkML system can only prove models in its internal format, it will be a castle in the air. Lagrange addressed this critical issue by adding support for the GGUF format. GGUF is the most widely adopted LLM model distribution format in communities such as Hugging Face. This means that developers can seamlessly import real-world, widely adopted models from the community into DeepProve for proof, greatly enhancing practicality and interoperability.
The fourth mountain:
Dedicated engine for autoregressive inference. The inference of LLM is 'autoregressive', meaning it generates one token at a time and needs to manage state between each step. This requires a dedicated inference driver capable of incrementally generating proofs and verifying the correctness of the output. DeepProve-1 introduces a dedicated module for this, achieving end-to-end, scalable proof of the LLM inference process.
Conquering these four mountains means that Lagrange has paved the way for proving more advanced models like LLAMA. What $LA drives is a powerful network that truly possesses industrial-grade verification capabilities for modern AI, with a technological moat that is unfathomably deep.
