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TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

arXiv – CS AI|Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath|March 3, 2026 at 05:00 AM|4 views

🤖AI Summary

TAO is a new verification protocol that enables users to verify neural network outputs from untrusted cloud services without requiring exact computation matches. The system uses tolerance-aware verification with IEEE-754 bounds and empirical profiles, implementing a dispute resolution mechanism deployed on Ethereum testnet.

Key Takeaways

→TAO solves the trust problem in ML-as-a-Service by allowing verification of neural network outputs without requiring bitwise equality.
→The protocol combines theoretical IEEE-754 bounds with empirical hardware profiles to create acceptance regions for valid outputs.
→A Merkle-anchored dispute game recursively isolates computation discrepancies to individual operators for efficient resolution.
→Implementation shows negligible overhead (0.3% on Qwen3-8B) while running on Ethereum Holesky testnet.
→Empirical thresholds are 100-1000x tighter than theoretical bounds across major GPU architectures.

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#tao #neural-networks #verification #ethereum #ml-as-a-service #floating-point #dispute-resolution #pytorch #gpu #blockchain

Read Original →via arXiv – CS AI

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