VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference

• URL: http://arxiv.org/abs/2509.24257v3
• Date: Mon, 10 Nov 2025 16:52:26 GMT
• Title: VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference
• Authors: Ke Wang, Zishuo Zhao, Xinyuan Song, Bill Shi, Libin Xia, Chris Tong, Lynn Ai, Felix Qu, Eric Yang,
• Abstract summary: We introduce VeriLLM, a publicly verifiable protocol for decentralized language models (LLMs) inference.<n>VeriLLM combines lightweight empirical rerunning with cryptographic commitments, allowing verifiers to validate results at approximately 1% of the underlying inference cost.<n>We show that VeriLLM achieves reliable public verifiability with minimal overhead.
• Score: 3.8760740008451156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decentralized inference provides a scalable and resilient paradigm for serving large language models (LLMs), enabling distributed resource utilization and reducing reliance on centralized providers. However, in a permissionless environment without trusted nodes, ensuring the correctness of model outputs remains a core challenge. We introduce VeriLLM, a publicly verifiable protocol for decentralized LLM inference that achieves security under a one-honest-verifier assumption while maintaining practical efficiency. VeriLLM combines lightweight empirical rerunning with cryptographic commitments, allowing verifiers to validate results at approximately 1% of the underlying inference cost. To prevent verification bottlenecks, we design an isomorphic inference-verification architecture that multiplexes both inference and verification roles across the same GPU workers. This design (i) improves GPU utilization and overall throughput, (ii) enlarges the effective validator set, enhancing robustness and liveness, and (iii) enforces task indistinguishability to prevent node-specific optimizations or selective behavior. Through theoretical analysis and system-level evaluation, we show that VeriLLM achieves reliable public verifiability with minimal overhead, offering a practical foundation for trustworthy and scalable decentralized LLM inference.

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