Archive
24 articles published in this period.
Probabilistic classifiers miss 30–40 % of policy violations. Lean 4 theorem provers and SMT solvers make certain guardrail tiers mathematically certain — here's how the four-layer policy stack works.
Five builders now win 96.7% of Ethereum blocks. Their edge isn't compute — it's exclusive orderflow: 12% of transactions that generate 54.59% of block value. AI agents transacting in public have to understand where they sit in that hierarchy — and when a preconfirmation changes the math.
Cross-chain bridges have lost $2.5B to hacks because receiving contracts accept messages they cannot cryptographically verify. AI agents operating across chains inherit this exact problem. Here are the four trust models, their real security parameters, and a principled agent policy.
A flash loan gives an AI agent $1M with no collateral — and demands it back plus 0.05% before the block ends. The atomic callback constraint is both what makes flash loans safe and what forces an agent to solve its entire strategy before touching the chain.
A new paper quantifies what everyone suspected: AI agent tokens trade at >10,000x the capital they actively manage. Here's the mechanics of why, and what it means for builders.
MORPH shows that a TPU v6e8 pod runs NTT at 40× CPU — 10× faster than an H100. The same silicon that accelerates AI inference now accelerates ZK proof generation, and the math that explains it is the same in both cases.
ZKLoRA lets a LoRA seller prove adapter compatibility with your base model without exposing the weights. The verifier checks in 1–2 s — but the prover bears 31–74 s per module, and the protocol is interactive, which puts trustless on-chain settlement just out of reach.
On-chain AI agents face a circular oracle problem: you need inference to decide if inference is worth calling. Here's what the token cost distribution actually looks like — and how to build around it.
Every ECDSA signature broadcasts the wallet's public key. A March 2026 Google/EF paper tightens the quantum break estimate to ~500k physical qubits — here's what that means for autonomous AI agent wallets.
Any permissionless network that rewards the best gradient contribution faces a Nash equilibrium where every rational miner copies instead of computes — same reward, zero cost. Gauntlet's commit-reveal mechanism closes that trap, and already trained a 1.2B LLM on Bittensor with real token payouts.
To borrow $100 in DeFi, you lock $125–167 in collateral. That gap is the price of trustlessness — and AI agents pay it the same as anyone. On-chain credit scoring is emerging to close it, but the signals that work for humans fail for agents that can spin up new addresses in milliseconds.
EIP-4844 blobs look like cheap on-chain storage, but they vanish in 18 days. Here is why DA windows break AI training pipelines and what to use instead.