Automating Zero-Knowledge Machine Learning Inference Verification in Distributed Computing Networks @inference_labs , @OpenGradient , @nesaorg The problem of verifying whether the results of artificial intelligence inference are correctly computed in a distributed computing network presents complex constraints that differ from a centralized server environment. In a structure where multiple participants run the same model in different hardware and software environments, it is difficult to assume the reliability of the computation results in advance. In this context, zero-knowledge machine learning has emerged as a technology that cryptographically proves that a specific input has been processed in a predetermined manner through a fixed model to generate a specific output. This method is evaluated as a suitable means for distributed environments because it can verify the legitimacy of execution without disclosing the computation process to the outside. However, artificial intelligence inference inherently contains non-determinism. Due to the non-associativity of floating-point operations, even the same operation can yield slight differences in results depending on the order of computation or hardware implementation, and these differences are more pronounced in GPU environments that use parallel operations. Distributed networks encompass a variety of devices, from consumer GPUs to specialized accelerators, leading to accumulated computational deviations due to differences in operating systems, drivers, memory architectures, and instruction sets. Cryptographic verification requires bit-level matches, so these environmental differences can directly lead to verification failures. To address this issue, zkML systems quantize floating-point operations to fixed-point and convert neural network operations into arithmetic circuit forms. Frameworks like JSTprove, EZKL, and RISC Zero express convolution, matrix multiplication, activation functions, etc., as a set of constraints, making the inference process provable. This process leads to a rapid increase in circuit complexity depending on the model's depth and size, and the time and memory usage required for proof generation also increase non-linearly. According to actual measurements, zkML proofs for the entire model require thousands to tens of thousands of times higher costs than recalculating the same inference. This cost structure acts as a key constraint when designing automated inference verification in distributed networks. If the cost of generating proofs is greater than the cost of doubting the inference results, verification loses its economic significance. Accordingly, actual systems adopt strategies that selectively verify only core operations or sensitive parts instead of proving the entire model. Inference Labs' DSperse reduces memory usage and proof costs significantly by decomposing the model into several slices and circuiting only the parts that are particularly important for reliability. Although this method does not provide a complete proof for the entire computation, it contributes to securing verification efficiency relative to cost. Automated verification pipelines are constructed based on these selective verification strategies. Inference Labs' JSTprove pipeline converts the model to fixed-point, compiles the ONNX format graph into arithmetic circuits, and generates proofs through a GKR-based proof system. The generated proofs are verified on-chain or off-chain, and in practice, multiple proofs are processed periodically in specific distributed networks. OpenGradient has designed a parallel execution structure called PIPE to handle multiple inference requests simultaneously, allowing the selection of either zkML, trusted execution environments, or execution without verification for each request. This adjustment ensures that verification costs do not become a direct bottleneck for block generation or overall throughput. Nesa adopts an approach that combines verification automation with computational protection. It uses a structure that distributes input data in an encrypted state across multiple nodes and performs inference in encrypted fragment units, aggregating the results. In this process, node selection and role distribution are adjusted through verifiable randomness and threshold cryptographic techniques, suppressing fraud through a procedure consisting of commit and reveal phases. This method ensures not only the integrity of inference results but also the confidentiality of inputs and model parameters. Integrating zkML-based verification into distributed computing networks clarifies the separation of roles between execution and verification. OpenGradient's parallelization strategy allows for the simultaneous processing of multiple inferences while managing the verification process separately, and Nesa's coordination layer is responsible for role distribution and incentives among nodes. Inference Labs' proof layer is tasked with cryptographically confirming that the actual computation was performed correctly. As each layer becomes separated, automated verification is implemented as a combination of multiple components rather than a single technology. The incentive structure is also a key element of automation. Nesa encourages participating nodes to act honestly through staking and commit-reveal structures, while Inference Labs distributes rewards based on proof generation capability and accuracy. OpenGradient's digital twin-based service converts the rights to access verified inference results into economic value. This structure is designed to maintain a certain level of trust even without a central administrator. Nevertheless, there are clear limitations to automated zkML inference verification. The asymmetry between the cost of generating incorrect results and the cost of proving correct results leaves room for potential attacks. Errors in the circuit conversion process, delays in proof generation, collusion among nodes, and computational deviations due to hardware errors cannot be completely eliminated with current technology. Systems mitigate these risks through replicated execution, reputation, and economic sanctions, but they cannot fundamentally eliminate them. In summary, automating zero-knowledge machine learning inference verification in distributed computing networks demonstrates clear technical achievements in cryptographically securing the reliability of computations. At the same time, it reveals structural limitations such as high proof costs, environmental constraints, and economic asymmetries. Current approaches implement practical automation by combining selective proofs, parallel execution, encrypted computation, and incentive design, which is evaluated as a case that brings the verifiability of artificial intelligence inference within a realistic range in distributed environments. $NESA