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6/ Paper #1
Demonstrating the Accuracy of an AI Model through Zero-Knowledge Proof is Even Faster Than Direct Computation.

Validation is not recomputation.
Freivalds-style checks and related math let you verify outputs cheaper than rerunning the model.
Includes a worked path for https://twitter.com/arpaofficial/status/1980333067895206190
7/ Paper #2
ZK-SNARK Verifiable Machine Learning.

What zk-snarks are, how they’re built, and how to program with them.
Arithmetization, proof systems, commitments.
Paths using Halo2, EZKL, Circom, circomlib-ml, keras2circom.
A blueprint for wrapping ML pipelines in proofs

Read https://twitter.com/arpaofficial/status/1980333069497512045
8/ Under the hood for builders:
Convolution, ReLU, and dense layers expressed as circuits via sumcheck and GKR.
Quantization from float to fixed point for ZK circuits with care for precision.
Off chain proving paired with on chain verification for open attestations. https://twitter.com/arpaofficial/status/1980333071091327241
We’re building the infrastructure Web3’s been waiting for. From verifiable AI, private compute, to cross-chain randomness 🎲

Check out the deep dive by @CoinMarketCap on how our next-gen zero-knowledge tech stacks up 🛠️: https://t.co/e2riVenmZX https://twitter.com/arpaofficial/status/1981136484322423179
🔍 Dive into the future of AI 🤖

Our CEO @felixmxu's latest piece on @hackernoon explores why AI must learn to verify itself, from algorithmic proof to trustless inference 🌐

See how ARPA Network is leading the charge in verifiable AI.

👉 https://t.co/W3Q6lrtzSP https://twitter.com/arpaofficial/status/1982616486343823570
2025/10/28 02:39:47
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