A small primitive operator set can lead to computational graphs with hundreds or thousands of operations, making them slow to execute and prove.

To address this, we will:

• Develop compilers that fuse multiple primitive operations into efficient composite operators.
• Add support for common ML operators like MatMul, SoftMax, and ReLU.
• Extend the AIR with specialized components for these new operators.

Currently, LuminAIR is written in Rust, requiring users to interact with it through Rust code. A Python SDK will be developed to make LuminAIR more accessible to data scientists and ML practitioners who are more familiar with Python.

This task will enable proof verification directly in web browsers using WebAssembly (WASM).

Deploy the LuminAIR verifier as a smart contract for decentralized proof verification. Possible approaches include:

• Implementing the verifier as a Cairo program verified on Starknet.
• Leveraging Ethereum’s crypto-economic security via AVSs networks like Aligned Layer.

Integrate support for Icicle-Stwo, which enables GPU acceleration via CUDA backends. This will significantly improve the speed of proof generation for large-scale models