Artemis: Efficient Commit-and-Prove SNARKs for zkML

Artemis addresses a critical bottleneck in Zero-Knowledge Machine Learning (zkML): the high cost of committing to large witness data (weights and activations). While the project has 0 stars and 5 forks, its value lies in the academic contribution (arXiv:2409.12055) rather than community traction. It competes in a high-density niche alongside projects like EZKL, Modulus Labs' Orion, and various GKR-based systems. The defensibility is low because it is a research-grade implementation without a developer ecosystem or 'moat' beyond the specific mathematical techniques described. Frontier labs (OpenAI, Anthropic) are unlikely to build this directly, but infrastructure providers like RISC Zero or Succinct could easily absorb these optimizations into their more generalized ZKVMs. The displacement horizon is relatively short (1-2 years) because the zkML field is rapidly shifting toward newer commitment schemes (like those based on Brakedown or Binius) and hardware-accelerated proving, which may solve the same overhead issues through different architectural choices.