\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party

Researchers introduce Range-Arithmetic, a novel framework enabling efficient verification of deep neural network inference performed by untrusted parties without re-execution. The method converts non-arithmetic operations into verifiable arithmetic steps using sum-check protocols, reducing computational overhead for both verification and inference while maintaining compatibility with blockchain-based proof systems.

Range-Arithmetic addresses a critical bottleneck in decentralized machine learning infrastructure. As blockchains struggle with computational capacity, offloading DNN inference to external parties becomes necessary, yet creates a trust problem—how can users verify correctness without repeating expensive calculations? This research solves that problem through clever cryptographic techniques that avoid traditional verification methods' inefficiencies.

The innovation builds on growing interest in verifiable computing within blockchain ecosystems, where computational integrity has become essential as applications grow more complex. Previous approaches either required Boolean encoding (computationally expensive) or high-degree polynomials (verification overhead). Range-Arithmetic sidesteps these limitations by converting operations like ReLU and fixed-point rounding directly into arithmetic verifiable through established sum-check protocols and range proofs—a more elegant approach.

For the blockchain and decentralized AI market, this matters substantially. It reduces three critical costs simultaneously: verification complexity, inference computational burden, and communication overhead. This efficiency gain directly impacts the viability of on-chain AI systems and makes outsourced computation more economically feasible for both service providers and users. The compatibility with finite-field proof systems means integration with existing blockchain infrastructure is straightforward.

Developers building decentralized ML systems should monitor how this framework performs at scale. Real-world deployment will reveal whether the theoretical efficiency gains translate to practical systems. The next phase involves integration with specific blockchain networks and benchmarking against production workloads.

• →Range-Arithmetic enables cryptographic verification of DNN inference without re-execution, critical for blockchain-based ML systems.
• →The framework reduces verification costs, inference computational requirements, and network communication overhead simultaneously.
• →The approach avoids Boolean encoding and high-degree polynomials, using sum-check protocols and concatenated range proofs instead.
• →Compatibility with finite-field proof systems enables straightforward integration with existing blockchain infrastructure.
• →Performance matches existing approaches while delivering significant efficiency improvements for decentralized AI applications.