# Software/algorithm Side: Model Verification > ⚠️ **Evaluation in Progress** > > Some of the mechanisms described on this page are currently **being evaluated and validated**. They represent design options under consideration, and may be refined, replaced, or omitted as the system evolves. In addition to hardware-level TEE-based solutions for model verification, we will discuss our algorithmic approach to ensure model integrity. In decentralized inference systems, verifying that each node accurately executes the intended model is crucial for maintaining the integrity and reliability of the system. This verification process ensures consistency in inference results across the network, safeguards against malicious modifications of the model, and ensures adherence to privacy protocols and regulatory compliance. Moreover, it optimizes the use of computational resources, preventing wastage on incorrect or unauthorized computations, and helps manage operational costs effectively. In the following section, we will introduce our use of z**ero-knowledge machine learning (ZKML)** and **consensus-based distribution verification (CDV)** for model verification. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.nesa.ai/nesa/major-innovations/private-inference-for-ai/background-and-exploratory-notes/software-algorithm-side-model-verification.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.