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6 articles published in this period.
Speculative decoding accelerates LLM inference 2–4× by splitting work between a cheap draft model and an expensive verifier. When those run on different nodes in a decentralized network, three properties of the protocol that are trivially safe on one machine become open attack surfaces.
A1 generates working exploits against 63% of real-world DeFi contracts. The attack costs ~$6k to break even; a comprehensive audit costs $60k. That 10× gap is the new threat model for every team deploying on a public chain.
Training Llama-3.1-8B costs ~$6M. The weights are free to clone. Perinucleus sampling embeds 24,576 distinct fingerprints per model with no quality loss — and a Poseidon ZK commitment registers ownership on-chain for $0.017, enforceable by smart contract.
Every zkSNARK bottoms out in a pairing check. Before Pectra you had BN254 at ~100-bit security. EIP-2537 added BLS12-381 at 128 bits, and its pairings are cheaper: 32,600k+37,700 vs 34,000k+45,000 gas. G2 MUL dropped from millions to 22,500 gas.
MoE routers let inference providers silently halve the experts they activate — saving 31% of compute while staying undetected by every current fingerprinting scheme.
Ethereum mainnet gas averaged 0.16 Gwei in June 2026 — a 3,000× drop from DeFi summer 2021. That collapse doesn't just cut costs; it invalidates five years of AI agent design assumptions and opens architectures that were economically impossible until now.