As AI models become increasingly integral to critical infrastructure—from financial underwriting to medical diagnosis—the question of verification becomes paramount. How can a user trust that a model was trained on a specific dataset? How can a developer verify that a compute provider didn't cut corners to save energy?
The Black Box Problem
Currently, AI training is a black box. We trust OpenAI, Google, or Anthropic because of their reputation. But in a decentralized world, or an adversarial one, reputation is insufficient. We need mathematical proof.
ZK Proof-of-Training (PoT)
Zektra introduces Zero-Knowledge Proof-of-Training (ZK-PoT). This protocol generates a cryptographic succinct proof (zk-SNARK/STARK) alongside the trained model weights. This proof certifies:
- The correct dataset was used (via cryptographic commitment).
- The specified model architecture was followed.
- Every floating-point operation (FLOP) in the forward and backward pass was executed correctly.
Eliminating Trust
With ZK-PoT, verifyers do not need to re-run the massive training job to check correctness. They simply verify the small cryptographic proof in milliseconds. This enables a "trustless" compute market where anyone can contribute hardware, and anyone can consume compute, without needing legal contracts or audits.
This technology is the bedrock of Zektra's decentralized compute layer, ensuring that even anonymous nodes can be trusted with high-stakes model training tasks.