REVIEW 2 cited by
opML: Optimistic Machine Learning on Blockchain
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In this context, we introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. This mechanism ensures decentralized and verifiable consensus for ML services, enhancing trust and transparency. Unlike zkML (Zero-Knowledge Machine Learning), opML offers cost-efficient and highly efficient ML services, with minimal participation requirements. Remarkably, opML enables the execution of extensive language models, such as 7B-LLaMA, on standard PCs without GPUs, significantly expanding accessibility. By combining the capabilities of blockchain and AI through opML, we embark on a transformative journey toward accessible, secure, and efficient on-chain machine learning.
Forward citations
Cited by 2 Pith papers
-
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
A tolerance-aware optimistic verification protocol makes floating-point neural-network inference verifiable on heterogeneous GPUs by accepting outputs within per-operator error bounds and resolving disputes via a Merk...
-
Integrity of peer-to-peer distributed LLM inference under malicious nodes
Under a simulated isotropic noise model, a canary-trap activation-drift detector achieves perfect AUROC separation of one malicious shard in multi-hop LLM inference.
Discussion (0). Continue with ORCID to comment.